Thanks to this Professional master’s degree 100% online, you will master the most innovative Artificial Intelligence techniques to optimize treatments in Aesthetic Medicine significantly”

According to a recent report by the World Health Organization, the increasing prevalence of Body Image Disorders has led to a 20% increase in the demand for Aesthetic Medicine procedures internationally. In response to this demand, Artificial Intelligence is emerging as a profitable technological solution capable of addressing the individual needs of patients through machine learning algorithms and analysis of large volumes of data. Faced with this reality, professionals need to keep abreast of the latest trends in this field in order to provide more individualized and efficient treatments.

In this context, TECH Global University has created an innovative Professional master’s degree in Artificial Intelligence in Aesthetic Medicine. Conceived by references in this area, the academic itinerary will delve into factors ranging from the life cycle of data or sophisticated techniques for the interpretation of large volumes of information to the implementation of algorithms using state-of-the-art software. At the same time, the syllabus will offer practitioners multiple strategies to carry out comprehensive diagnoses of conditions such as precancerous lesions, melanomas or acne using neural networks and even computer vision. Also, the didactic materials will delve into the use of different technological instruments to provide individuals with optimal clinical post-treatment follow-up in real time. Therefore, graduates will acquire advanced skills to master deep learning techniques to optimize their aesthetic procedures and ensure an improvement in the overall well-being of individuals.

In addition, TECH Global Universityy offers a 100% online educational environment, tailored to the needs of practicing physicians seeking to advance their careers. It also employs its innovative Relearning system, based on the natural and progressive repetition of key concepts to effectively fix knowledge. Graduates will have access to a library full of multimedia resources in different audiovisual formats, such as interactive summaries, explanatory videos and infographics.

You will handle Image Processing technologies to plan therapeutic beauty plans in an individualized way, adapting to the needs of individuals”

This Professional master’s degree in Artificial Intelligence in Aesthetic Medicine contains the most complete and up-to-date scientific program on the market.The most important features include:

• The development of case studies presented by experts in Artificial Intelligence applied to Aesthetic Medicine
• The graphic, schematic and eminently practical contents with which it is conceived gather scientific and practical information on those disciplines that are indispensable for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Are you looking to integrate Artificial Intelligence solutions into your daily practice to automate complex repetitive tasks? Achieve it with this program in just 12 months”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this specializing program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned experts.

You will delve into various methods for collecting, managing and analyzing large volumes of clinical data; thus improving informed strategic decision making"

The innovative Relearning methodology powered by TECH Global University will provide you with the opportunity to individually plan your schedule and pace of study"

The didactic contents that make up this program will provide physicians with a comprehensive knowledge of the use of Artificial Intelligence in Aesthetic Medicine. Accordingly, the syllabus will delve into issues ranging from the use of algorithms to obtain valuable clinical insights from large amounts of data or the development of predictive models with advanced software such as TensorFlow to security techniques to ensure the protection of confidential patient information. Thanks to this, graduates will be able to implement emerging technologies in their daily practice to improve operational efficiency and quality of services.

You will skillfully use the Thermage FLX to adjust the body radiofrequency of therapies according to the specific characteristics of the users' skin”

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

1.1.1. When Do We Start Talking About Artificial Intelligence?
1.1.2. References in Film
1.1.3. Importance of Artificial Intelligence
1.1.4. Technologies that Enable and Support Artificial Intelligence

1.2. Artificial Intelligence in Games

1.2.1. Game Theory
1.2.2. Minimax and Alpha-Beta Pruning
1.2.3. Simulation: Monte Carlo

1.3. Neural Networks

1.3.1. Biological Fundamentals
1.3.2. Computational Model
1.3.3. Supervised and Unsupervised Neural Networks
1.3.4. Simple Perceptron
1.3.5. Multilayer Perceptron

1.4. Genetic Algorithms

1.4.1. History
1.4.2. Biological Basis
1.4.3. Problem Coding
1.4.4. Generation of the Initial Population
1.4.5. Main Algorithm and Genetic Operators
1.4.6. Evaluation of Individuals: Fitness

1.5. Thesauri, Vocabularies, Taxonomies

1.5.1. Vocabulary
1.5.2. Taxonomy
1.5.3. Thesauri
1.5.4. Ontologies
1.5.5. Knowledge Representation: Semantic Web

1.6. Semantic Web

1.6.1. Specifications: RDF, RDFS and OWL
1.6.2. Inference/ Reasoning
1.6.3. Linked Data

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.2. Decision Support Systems

1.8. Chatbots and Virtual Assistants

1.8.1. Types of Assistants: Voice and Text Assistants
1.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialog Flow
1.8.3. Integrations: Web, Slack, Whatsapp, Facebook
1.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant

1.9. AI Implementation Strategy
1.10. Future of Artificial Intelligence

1.10.1. Understand How to Detect Emotions Using Algorithms
1.10.2. Creating a Personality: Language, Expressions and Content
1.10.3. Trends of Artificial Intelligence
1.10.4. Reflections

Module 2. Data Types and Data Life Cycle

2.1. Statistics

2.1.1. Statistics: Descriptive Statistics, Statistical Inferences
2.1.2. Population, Sample, Individual
2.1.3. Variables: Definition, Measurement Scales

2.2. Types of Data Statistics

2.2.1. According to Type

2.2.1.1. Quantitative: Continuous Data and Discrete Data
2.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data

2.2.2. According to Its Shape

2.2.2.1. Numeric
2.2.2.2. Text
2.2.2.3. Logical

2.2.3. According to Its Source

2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Life Cycle of Data

2.3.1. Stages of the Cycle
2.3.2. Milestones of the Cycle
2.3.3. FAIR Principles

2.4. Initial Stages of the Cycle

2.4.1. Definition of Goals
2.4.2. Determination of Resource Requirements
2.4.3. Gantt Chart
2.4.4. Data Structure

2.5. Data Collection

2.5.1. Methodology of Data Collection
2.5.2. Data Collection Tools
2.5.3. Data Collection Channels

2.6. Data Cleaning

2.6.1. Phases of Data Cleansing
2.6.2. Data Quality
2.6.3. Data Manipulation (with R)

2.7. Data Analysis, Interpretation and Evaluation of Results

2.7.1. Statistical Measures
2.7.2. Relationship Indexes
2.7.3. Data Mining

2.8. Datawarehouse

2.8.1. Elements that Comprise It
2.8.2. Design
2.8.3. Aspects to Consider

2.9. Data Availability

2.9.1. Access
2.9.2. Uses
2.9.3. Security

2.10. Regulatory Framework

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Regulatory Aspects

Module 3. Data in Artificial Intelligence

3.1. Data Science

3.1.1. Data Science
3.1.2. Advanced Tools for Data Scientists

3.2. Data, Information and Knowledge

3.2.1. Data, Information and Knowledge
3.2.2. Types of Data
3.2.3. Data Sources

3.3. From Data to Information

3.3.1. Data Analysis
3.3.2. Types of Analysis
3.3.3. Extraction of Information from a Dataset

3.4. Extraction of Information Through Visualization

3.4.1. Visualization as an Analysis Tool
3.4.2. Visualization Methods
3.4.3. Visualization of a Data Set

3.5. Data Quality

3.5.1. Quality Data
3.5.2. Data Cleaning
3.5.3. Basic Data Pre-Processing

3.6. Dataset

3.6.1. Dataset Enrichment
3.6.2. The Curse of Dimensionality
3.6.3. Modification of Our Data Set

3.7. Unbalance

3.7.1. Classes of Unbalance
3.7.2. Unbalance Mitigation Techniques
3.7.3. Balancing a Dataset

3.8. Unsupervised Models

3.8.1. Unsupervised Model
3.8.2. Methods
3.8.3. Classification with Unsupervised Models

3.9. Supervised Models

3.9.1. Supervised Model
3.9.2. Methods
3.9.3. Classification with Supervised Models

3.10. Tools and Good Practices

3.10.1. Good Practices for Data Scientists
3.10.2. The Best Model
3.10.3. Useful Tools

Module 4. Data Mining: Selection, Pre-Processing and Transformation

4.1. Statistical Inference

4.1.1. Descriptive Statistics vs. Statistical Inference
4.1.2. Parametric Procedures
4.1.3. Non-Parametric Procedures

4.2. Exploratory Analysis

4.2.1. Descriptive Analysis
4.2.2. Visualization
4.2.3. Data Preparation

4.3. Data Preparation

4.3.1. Integration and Data Cleaning
4.3.2. Normalization of Data
4.3.3. Transforming Attributes

4.4. Missing Values

4.4.1. Treatment of Missing Values
4.4.2. Maximum Likelihood Imputation Methods
4.4.3. Missing Value Imputation Using Machine Learning

4.5. Noise in the Data

4.5.1. Noise Classes and Attributes
4.5.2. Noise Filtering
4.5.3. The Effect of Noise

4.6. The Curse of Dimensionality

4.6.1. Oversampling
4.6.2. Undersampling
4.6.3. Multidimensional Data Reduction

4.7. From Continuous to Discrete Attributes

4.7.1. Continuous Data vs. Discreet Data
4.7.2. Discretization Process

4.8. The Data

4.8.1. Data Selection
4.8.2. Prospects and Selection Criteria
4.8.3. Selection Methods

4.9. Instance Selection

4.9.1. Methods for Instance Selection
4.9.2. Prototype Selection
4.9.3. Advanced Methods for Instance Selection

4.10. Data Pre-Processing in Big Data Environments

Module 5. Algorithm and Complexity in Artificial Intelligence

5.1. Introduction to Algorithm Design Strategies

5.1.1. Recursion
5.1.2. Divide and Conquer
5.1.3. Other Strategies

5.2. Efficiency and Analysis of Algorithms

5.2.1. Efficiency Measures
5.2.2. Measuring the Size of the Input
5.2.3. Measuring Execution Time
5.2.4. Worst, Best and Average Case
5.2.5. Asymptotic Notation
5.2.6. Mathematical Analysis Criteria for Non-Recursive Algorithms
5.2.7. Mathematical Analysis of Recursive Algorithms
5.2.8. Empirical Analysis of Algorithms

5.3. Sorting Algorithms

5.3.1. Concept of Sorting
5.3.2. Bubble Sorting
5.3.3. Sorting by Selection
5.3.4. Sorting by Insertion
5.3.5. Merge Sort
5.3.6. Quick Sort

5.4. Algorithms with Trees

5.4.1. Tree Concept
5.4.2. Binary Trees
5.4.3. Tree Paths
5.4.4. Representing Expressions
5.4.5. Ordered Binary Trees
5.4.6. Balanced Binary Trees

5.5. Algorithms Using Heaps

5.5.1. Heaps
5.5.2. The Heapsort Algorithm
5.5.3. Priority Queues

5.6. Graph Algorithms

5.6.1. Representation
5.6.2. Traversal in Width
5.6.3. Depth Travel
5.6.4. Topological Sorting

5.7. Greedy Algorithms

5.7.1. Greedy Strategy
5.7.2. Elements of the Greedy Strategy
5.7.3. Currency Exchange
5.7.4. Traveler’s Problem
5.7.5. Backpack Problem

5.8. Minimal Path Finding

5.8.1. The Minimum Path Problem
5.8.2. Negative Arcs and Cycles
5.8.3. Dijkstra's Algorithm

5.9. Greedy Algorithms on Graphs

5.9.1. The Minimum Covering Tree
5.9.2. Prim's Algorithm
5.9.3. Kruskal’s Algorithm
5.9.4. Complexity Analysis

5.10. Backtracking

5.10.1. Backtracking
5.10.2. Alternative Techniques

Module 6. Intelligent Systems

6.1. Agent Theory

6.1.1. Concept History
6.1.2. Agent Definition
6.1.3. Agents in Artificial Intelligence
6.1.4. Agents in Software Engineering

6.2. Agent Architectures

6.2.1. The Reasoning Process of an Agent
6.2.2. Reactive Agents
6.2.3. Deductive Agents
6.2.4. Hybrid Agents
6.2.5. Comparison

6.3. Information and Knowledge

6.3.1. Difference between Data, Information and Knowledge
6.3.2. Data Quality Assessment
6.3.3. Data Collection Methods
6.3.4. Information Acquisition Methods
6.3.5. Knowledge Acquisition Methods

6.4. Knowledge Representation

6.4.1. The Importance of Knowledge Representation
6.4.2. Definition of Knowledge Representation According to Roles
6.4.3. Knowledge Representation Features

6.5. Ontologies

6.5.1. Introduction to Metadata
6.5.2. Philosophical Concept of Ontology
6.5.3. Computing Concept of Ontology
6.5.4. Domain Ontologies and Higher-Level Ontologies
6.5.5. How to Build an Ontology

6.6. Ontology Languages and Ontology Creation Software

6.6.1. Triple RDF, Turtle and N
6.6.2. RDF Schema
6.6.3. OWL
6.6.4. SPARQL
6.6.5. Introduction to Ontology Creation Tools
6.6.6. Installing and Using Protégé

6.7. Semantic Web

6.7.1. Current and Future Status of the Semantic Web
6.7.2. Semantic Web Applications

6.8. Other Knowledge Representation Models

6.8.1. Vocabulary
6.8.2. Global Vision
6.8.3. Taxonomy
6.8.4. Thesauri
6.8.5. Folksonomy
6.8.6. Comparison
6.8.7. Mind Maps

6.9. Knowledge Representation Assessment and Integration

6.9.1. Zero-Order Logic
6.9.2. First-Order Logic
6.9.3. Descriptive Logic
6.9.4. Relationship between Different Types of Logic
6.9.5. Prolog: Programming Based on First-Order Logic

6.10. Semantic Reasoners, Knowledge-Based Systems and Expert Systems

6.10.1. Concept of Reasoner
6.10.2. Reasoner Applications
6.10.3. Knowledge-Based Systems
6.10.4. MYCIN: History of Expert Systems
6.10.5. Expert Systems Elements and Architecture
6.10.6. Creating Expert Systems

Module 7. Machine Learning and Data Mining

7.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning

7.1.1. Key Concepts of Knowledge Discovery Processes
7.1.2. Historical Perspective of Knowledge Discovery Processes
7.1.3. Stages of the Knowledge Discovery Processes
7.1.4. Techniques Used in Knowledge Discovery Processes
7.1.5. Characteristics of Good Machine Learning Models
7.1.6. Types of Machine Learning Information
7.1.7. Basic Learning Concepts
7.1.8. Basic Concepts of Unsupervised Learning

7.2. Data Exploration and Pre-Processing

7.2.1. Data Processing
7.2.2. Data Processing in the Data Analysis Flow
7.2.3. Types of Data
7.2.4. Data Transformations
7.2.5. Visualization and Exploration of Continuous Variables
7.2.6. Visualization and Exploration of Categorical Variables
7.2.7. Correlation Measures
7.2.8. Most Common Graphic Representations
7.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction

7.3. Decision Trees

7.3.1. ID Algorithm
7.3.2. Algorithm C
7.3.3. Overtraining and Pruning
7.3.4. Result Analysis

7.4. Evaluation of Classifiers

7.4.1. Confusion Matrixes
7.4.2. Numerical Evaluation Matrixes
7.4.3. Kappa Statistic
7.4.4. ROC Curves

7.5. Classification Rules

7.5.1. Rule Evaluation Measures
7.5.2. Introduction to Graphic Representation
7.5.3. Sequential Overlay Algorithm

7.6. Neural Networks

7.6.1. Basic Concepts
7.6.2. Simple Neural Networks
7.6.3. Backpropagation Algorithm
7.6.4. Introduction to Recurrent Neural Networks

7.7. Bayesian Methods

7.7.1. Basic Probability Concepts
7.7.2. Bayes' Theorem
7.7.3. Naive Bayes
7.7.4. Introduction to Bayesian Networks

7.8. Regression and Continuous Response Models

7.8.1. Simple Linear Regression
7.8.2. Multiple Linear Regression
7.8.3. Logistic Regression
7.8.4. Regression Trees
7.8.5. Introduction to Support Vector Machines (SVM)
7.8.6. Goodness-of-Fit Measures

7.9. Clustering

7.9.1. Basic Concepts
7.9.2. Hierarchical Clustering
7.9.3. Probabilistic Methods
7.9.4. EM Algorithm
7.9.5. B-Cubed Method
7.9.6. Implicit Methods

7.10. Text Mining and Natural Language Processing (NLP)

7.10.1. Basic Concepts
7.10.2. Corpus Creation
7.10.3. Descriptive Analysis
7.10.4. Introduction to Feelings Analysis

Module 8. Neural Networks, the Basis of Deep Learning

8.1. Deep Learning

8.1.1. Types of Deep Learning
8.1.2. Applications of Deep Learning
8.1.3. Advantages and Disadvantages of Deep Learning

8.2. Operations

8.2.1. Sum
8.2.2. Product
8.2.3. Transfer

8.3. Layers

8.3.1. Input Layer
8.3.2. Hidden Layer
8.3.3. Output Layer

8.4. Union of Layers and Operations

8.4.1. Architecture Design
8.4.2. Connection between Layers
8.4.3. Forward Propagation

8.5. Construction of the First Neural Network

8.5.1. Network Design
8.5.2. Establish the Weights
8.5.3. Network Training

8.6. Trainer and Optimizer

8.6.1. Optimizer Selection
8.6.2. Establishment of a Loss Function
8.6.3. Establishing a Metric

8.7. Application of the Principles of Neural Networks

8.7.1. Activation Functions
8.7.2. Backward Propagation
8.7.3. Parameter Adjustment

8.8. From Biological to Artificial Neurons

8.8.1. Functioning of a Biological Neuron
8.8.2. Transfer of Knowledge to Artificial Neurons
8.8.3. Establish Relations Between the Two

8.9. Implementation of MLP (Multilayer Perceptron) with Keras

8.9.1. Definition of the Network Structure
8.9.2. Model Compilation
8.9.3. Model Training

8.10. Fine Tuning Hyperparameters of Neural Networks

8.10.1. Selection of the Activation Function
8.10.2. Set the Learning Rate
8.10.3. Adjustment of Weights

Module 9. Deep Neural Networks Training

9.1. Gradient Problems

9.1.1. Gradient Optimization Techniques
9.1.2. Stochastic Gradients
9.1.3. Weight Initialization Techniques

9.2. Reuse of Pre-Trained Layers

9.2.1. Transfer Learning Training
9.2.2. Feature Extraction
9.2.3. Deep Learning

9.3. Optimizers

9.3.1. Stochastic Gradient Descent Optimizers
9.3.2. Optimizers Adam and RMSprop
9.3.3. Moment Optimizers

9.4. Learning Rate Programming

9.4.1. Automatic Learning Rate Control
9.4.2. Learning Cycles
9.4.3. Smoothing Terms

9.5. Overfitting

9.5.1. Cross Validation
9.5.2. Regularization
9.5.3. Evaluation Metrics

9.6. Practical Guidelines

9.6.1. Model Design
9.6.2. Selection of Metrics and Evaluation Parameters
9.6.3. Hypothesis Testing

9.7. Transfer Learning

9.7.1. Transfer Learning Training
9.7.2. Feature Extraction
9.7.3. Deep Learning

9.8. Data Augmentation

9.8.1. Image Transformations
9.8.2. Synthetic Data Generation
9.8.3. Text Transformation

9.9. Practical Application of Transfer Learning

9.9.1. Transfer Learning Training
9.9.2. Feature Extraction
9.9.3. Deep Learning

9.10. Regularization

9.10.1. L and L
9.10.2. Regularization by Maximum Entropy
9.10.3. Dropout

Module 10. Model Customization and Training with TensorFlow

10.1. TensorFlow

10.1.1. Use of the TensorFlow Library
10.1.2. Model Training with TensorFlow
10.1.3. Operations with Graphs in TensorFlow

10.2. TensorFlow and NumPy

10.2.1. NumPy Computing Environment for TensorFlow
10.2.2. Using NumPy Arrays with TensorFlow
10.2.3. NumPy Operations for TensorFlow Graphs

10.3. Model Customization and Training Algorithms

10.3.1. Building Custom Models with TensorFlow
10.3.2. Management of Training Parameters
10.3.3. Use of Optimization Techniques for Training

10.4. TensorFlow Features and Graphs

10.4.1. Functions with TensorFlow
10.4.2. Use of Graphs for Model Training
10.4.3. Graph Optimization with TensorFlow Operations

10.5. Loading and Preprocessing Data with TensorFlow

10.5.1. Loading Data Sets with TensorFlow
10.5.2. Pre-Processing Data with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tfdata API

10.6.1. Using the tf.data API for Data Processing
10.6.2. Construction of Data Streams with tf.data
10.6.3. Using the tf.data API for Model Training

10.7. The TFRecord Format

10.7.1. Using the TFRecord API for Data Serialization
10.7.2. TFRecord File Upload with TensorFlow
10.7.3. Using TFRecord Files for Model Training

10.8. Keras Pre-Processing Layers

10.8.1. Using the Keras Pre-Processing API
10.8.2. Pre-Processing Pipelined Construction with Keras
10.8.3. Using the Keras Pre-Processing API for Model Training

10.9. The TensorFlow Datasets Project

10.9.1. Using TensorFlow Datasets for Data Loading
10.9.2. Data Pre-Processing with TensorFlow Datasets
10.9.3. Using TensorFlow Datasets for Model Training

10.10. Building a Deep Learning App with TensorFlow

10.10.1. Practical Application
10.10.2. Building a Deep Learning App with TensorFlow
10.10.3. Model Training with TensorFlow
10.10.4. Using the Application for the Prediction of Results

Module 11. Deep Computer Vision with Convolutional Neural Networks

11.1. The Visual Cortex Architecture

11.1.1. Functions of the Visual Cortex
11.1.2. Theories of Computational Vision
11.1.3. Models of Image Processing

11.2. Convolutional Layers

11.2.1. Reuse of Weights in Convolution
11.2.2. Convolution D
11.2.3. Activation Functions

11.3. Grouping Layers and Implementation of Grouping Layers with Keras

11.3.1. Pooling and Striding
11.3.2. Flattening
11.3.3. Types of Pooling

11.4. CNN Architecture

11.4.1. VGG Architecture
11.4.2. AlexNet Architecture
11.4.3. ResNet Architecture

11.5. Implementing a CNN ResNet- Using Keras

11.5.1. Weight Initialization
11.5.2. Input Layer Definition
11.5.3. Output Definition

11.6. Use of Pre-Trained Keras Models

11.6.1. Characteristics of Pre-Trained Models
11.6.2. Uses of Pre-Trained Models
11.6.3. Advantages of Pre-Trained Models

11.7. Pre-Trained Models for Transfer Learning

11.7.1. Transfer Learning
11.7.2. Transfer Learning Process
11.7.3. Advantages of Transfer Learning

11.8. Deep Computer Vision Classification and Localization

11.8.1. Image Classification
11.8.2. Localization of Objects in Images
11.8.3. Object Detection

11.9. Object Detection and Object Tracking

11.9.1. Object Detection Methods
11.9.2. Object Tracking Algorithms
11.9.3. Tracking and Localization Techniques

11.10. Semantic Segmentation

11.10.1. Deep Learning for Semantic Segmentation
11.10.2. Edge Detection
11.10.3. Rule-Based Segmentation Methods

Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

12.1. Text Generation Using RNN

12.1.1. Training an RNN for Text Generation
12.1.2. Natural Language Generation with RNN
12.1.3. Text Generation Applications with RNN

12.2. Training Data Set Creation

12.2.1. Preparation of the Data for Training an RNN
12.2.2. Storage of the Training Dataset
12.2.3. Data Cleaning and Transformation
12.2.4. Sentiment Analysis

12.3. Classification of Opinions with RNN

12.3.1. Detection of Themes in Comments
12.3.2. Sentiment Analysis with Deep Learning Algorithms

12.4. Encoder-Decoder Network for Neural Machine Translation

12.4.1. Training an RNN for Machine Translation
12.4.2. Use of an Encoder-Decoder Network for Machine Translation
12.4.3. Improving the Accuracy of Machine Translation with RNNs

12.5. Attention Mechanisms

12.5.1. Application of Care Mechanisms in RNN
12.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models
12.5.3. Advantages of Attention Mechanisms in Neural Networks

12.6. Transformer Models

12.6.1. Using Transformers Models for Natural Language Processing
12.6.2. Application of Transformers Models for Vision
12.6.3. Advantages of Transformers Models

12.7. Transformers for Vision

12.7.1. Use of Transformers Models for Vision
12.7.2. Image Data Pre-Processing
12.7.3. Training a Transformers Model for Vision

12.8. Hugging Face’s Transformers Library

12.8.1. Using Hugging Face's Transformers Library
12.8.2. Hugging Face’s Transformers Library Application
12.8.3. Advantages of Hugging Face’s Transformers Library

12.9. Other Transformers Libraries. Comparison

12.9.1. Comparison Between Different Transformers Libraries
12.9.2. Use of the Other Transformers Libraries
12.9.3. Advantages of the Other Transformers Libraries

12.10. Development of an NLP Application with RNN and Attention. Practical Application

12.10.1. Development of a Natural Language Processing Application with RNN and Attention.
12.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application
12.10.3. Evaluation of the Practical Application

Module 13. Autoencoders, GANs, and Diffusion Models

13.1. Representation of Efficient Data

13.1.1. Dimensionality Reduction
13.1.2. Deep Learning
13.1.3. Compact Representations

13.2. PCA Realization with an Incomplete Linear Automatic Encoder

13.2.1. Training Process
13.2.2. Implementation in Python
13.2.3. Use of Test Data

13.3. Stacked Automatic Encoders

13.3.1. Deep Neural Networks
13.3.2. Construction of Coding Architectures
13.3.3. Use of Regularization

13.4. Convolutional Autoencoders

13.4.1. Design of Convolutional Models
13.4.2. Convolutional Model Training
13.4.3. Results Evaluation

13.5. Noise Suppression of Automatic Encoders

13.5.1. Filter Application
13.5.2. Design of Coding Models
13.5.3. Use of Regularization Techniques

13.6. Sparse Automatic Encoders

13.6.1. Increasing Coding Efficiency
13.6.2. Minimizing the Number of Parameters
13.6.3. Using Regularization Techniques

13.7. Variational Automatic Encoders

13.7.1. Use of Variational Optimization
13.7.2. Unsupervised Deep Learning
13.7.3. Deep Latent Representations

13.8. Generation of Fashion MNIST Images

13.8.1. Pattern Recognition
13.8.2. Image Generation
13.8.3. Deep Neural Networks Training

13.9. Generative Adversarial Networks and Diffusion Models

13.9.1. Content Generation from Images
13.9.2. Modeling of Data Distributions
13.9.3. Use of Adversarial Networks

13.10. Implementation of the Models

13.10.1. Practical Application
13.10.2. Implementation of the Models
13.10.3. Use of Real Data
13.10.4. Results Evaluation

Module 14. Bio-Inspired Computing

14.1. Introduction to Bio-Inspired Computing

14.1.1. Introduction to Bio-Inspired Computing

14.2. Social Adaptation Algorithms

14.2.1. Bio-Inspired Computation Based on Ant Colonies
14.2.2. Variants of Ant Colony Algorithms
14.2.3. Particle Cloud Computing

14.3. Genetic Algorithms

14.3.1. General Structure
14.3.2. Implementations of the Major Operators

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms

14.4.1. CHC Algorithm
14.4.2. Multimodal Problems

14.5. Evolutionary Computing Models (I)

14.5.1. Evolutionary Strategies
14.5.2. Evolutionary Programming
14.5.3. Algorithms Based on Differential Evolution

14.6. Evolutionary Computation Models (II)

14.6.1. Evolutionary Models Based on Estimation of Distributions (EDA)
14.6.2. Genetic Programming

14.7. Evolutionary Programming Applied to Learning Problems

14.7.1. Rules-Based Learning
14.7.2. Evolutionary Methods in Instance Selection Problems

14.8. Multi-Objective Problems

14.8.1. Concept of Dominance
14.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems

14.9. Neural Networks (I)

14.9.1. Introduction to Neural Networks
14.9.2. Practical Example with Neural Networks

14.10. Neural Networks (II)

14.10.1. Use Cases of Neural Networks in Medical Research
14.10.2. Use Cases of Neural Networks in Economics
14.10.3. Use Cases of Neural Networks in Artificial Vision

Module 15. Artificial Intelligence: Strategies and Applications

15.1. Financial Services

15.1.1. The Implications of Artificial Intelligence (AI) in Financial Services. Opportunities and Challenges
15.1.2. Case Studies
15.1.3. Potential Risks Related to the Use of AI
15.1.4. Potential Future Developments/Uses of AI

15.2. Implications of Artificial Intelligence in Healthcare Service

15.2.1. Implications of AI in the Healthcare Sector. Opportunities and Challenges
15.2.2. Case Studies

15.3. Risks Related to the Use of AI in Healthcare Service

15.3.1. Potential Risks Related to the Use of AI
15.3.2. Potential Future Developments/Uses of AI

15.4. Retail

15.4.1. Implications of AI in Retail. Opportunities and Challenges
15.4.2. Case Studies
15.4.3. Potential Risks Related to the Use of AI
15.4.4. Potential Future Developments/Uses of AI

15.5. Industry

15.5.1. Implications of AI in Industry. Opportunities and Challenges
15.5.2. Case Studies

15.6. Potential Risks Related to the Use of AI in Industry

15.6.1. Case Studies
15.6.2. Potential Risks Related to the Use of AI
15.6.3. Potential Future Developments/Uses of AI

15.7. Public Administration

15.7.1. AI Implications for Public Administration. Opportunities and Challenges
15.7.2. Case Studies
15.7.3. Potential Risks Related to the Use of AI
15.7.4. Potential Future Developments/Uses of AI

15.8. Educational

15.8.1. AI Implications for Education. Opportunities and Challenges
15.8.2. Case Studies
15.8.3. Potential Risks Related to the Use of AI
15.8.4. Potential Future Developments/Uses of AI

15.9. Forestry and Agriculture

15.9.1. Implications of AI in Forestry and Agriculture. Opportunities and Challenges
15.9.2. Case Studies
15.9.3. Potential Risks Related to the Use of AI
15.9.4. Potential Future Developments/Uses of AI

15.10. Human Resources

15.10.1. Implications of AI for Human Resources. Opportunities and Challenges
15.10.2. Case Studies
15.10.3. Potential Risks Related to the Use of AI
15.10.4. Potential Future Developments/Uses of AI

Module 16. Clinical Data Processing for Predictive Modeling in Aesthetic Medicine

16.1. Patient Data Collection and Storage

16.1.1. Database Implementation for Secure, Scalable Storage (MongoDB Atlas)
16.1.2. Facial and Body Image Data Collection (Google Cloud Vision AI)
16.1.3. Collection of Clinical History and Risk Factors (Epic Systems AI) 
16.1.4. Integration of Data from Medical Devices and Wearables (Fitbit Health Solutions)

16.2. Data Cleaning and Normalization for Predictive Modeling

16.2.1. Detection and Correction of Missing or Inconsistent Data (OpenRefine)
16.2.2. Normalization of Image and Clinical Text Data Formats (Pandas AI Library)
16.2.3. Elimination of Bias in Clinical and Aesthetic Data (IBM AI Fairness 360)
16.2.4. Pre-Processing and Organization of Data to Train Predictive Models (TensorFlow)

16.3. Medical Image Data Structuring

16.3.1. Facial Image Segmentation for Feature Analysis (NVIDIA Clara)
16.3.2. Identification and Classification of Skin Areas of Interest (SkinIO)
16.3.3. Organization of Image Data in Different Resolutions and Layers (Clarifai)
16.3.4. Labeling of Medical Images to Train Neural Networks (Labelbox)

16.4. Predictive Modeling Based on Personal Data

16.4.1. Prediction of Aesthetic Results from Historical Data (H2O.ai AutoML)
16.4.2. Machine Learning Models for Personalized Treatment (Amazon SageMaker)
16.4.3. Deep Neural Networks for Predicting Response to Treatments (DeepMind AlphaFold)
16.4.4. Personalization of Models according to Facial and Body Features (Google AutoML Vision)

16.5. Analysis of External and Environmental Factors in Aesthetic Results

16.5.1. Incorporation of Meteorological Data in Skin Analysis (Weather Company Data on IBM Cloud)
16.5.2. Modeling UV Exposure and Its Impact on the Skin (NOAA AI UV Index)
16.5.3. Integration of Lifestyle Factors in Predictive Models (WellnessFX AI)
16.5.4. Analysis of Interactions between Environmental Factors and Treatments (Proven Skincare AI)

16.6. Generation of Synthetic Data for Training

16.6.1. Synthetic Data Creation to Improve Model Training (Synthea)
16.6.2. Synthetic Imaging of Rare Skin Conditions (NVIDIA GANs)
16.6.3. Simulation of Variations in Skin Textures and Skin Tones (DataGen)
16.6.4. Use of Synthetic Data to Avoid Privacy Concerns (Synthetic Data Vault)

16.7. Anonymization and Security of Patient Data

16.7.1. Implementation of Clinical Data Anonymization Techniques (OneTrust)
16.7.2. Encryption of Sensitive Data in Patient Databases (AWS Key Management Service)
16.7.3. Pseudonymization to Protect Personal Data in AI Models (Microsoft Azure AI Privacy)
16.7.4. Auditing and Monitoring Access to Patient Data (Datadog AI Security)

16.8. Optimization of Predictive Models for Personalization of Treatment

16.8.1. Selection of Predictive Algorithms Based on Structured Data (DataRobot)
16.8.2. Optimization of Hyperparameters in Predictive Models (Keras Tuner)
16.8.3. Cross-Validation and Testing of Customized Models (Scikit-learn)
16.8.4. Model Fitting based on Outcome Feedback (MLflow)

16.9. Data Visualization and Predictive Results

16.9.1. Creating Visualization Dashboards for Predictive Results (Tableau)
16.9.2. Treatment Progression Charts and Long-Term Predictions (Power BI)
16.9.3. Visualization of Multivariate Analysis on Patient Data (Plotly)
16.9.4. Comparison of Results between Different Predictive Models (Looker)

16.10. Updating and Maintaining Predictive Models with New Data

16.10.1. Continuous Integration of New Data into Trained Models (Google Vertex AI Pipelines)
16.10.2. Performance Monitoring and Automatic Adjustments in Models (IBM Watson Machine Learning)
16.10.3. Updating Predictive Models Based on Recent Data Patterns (Amazon SageMaker Model Monitor)
16.10.4. Real-Time Feedback for Continuous Model Improvement (Dataiku)

Module 17. Modeling and Simulation in Aesthetic Medicine

17.1. Procedure Simulation with Artificial Intelligence

17.1.1. 3D Simulation of Facial Changes in Rejuvenation Procedures (Crisalix)
17.1.2. Modeling Dermal Fillers Results and Lip Adjustments (Modiface)
17.1.3. Visualization of Body Aesthetic Surgery Results (MirrorMe3D)
17.1.4. Real-Time Projection of Botox and Fillers Results (TouchMD)

17.2. Creating 3D Patient Models

17.2.1. Generating 3D Facial Models from Photographs (FaceGen)
17.2.2. 3D Body Scanning and Reconstruction for Aesthetic Simulation (Artec Eva)
17.2.3. Integration of Anatomical Data into 3D Models (Materialise Mimics)
17.2.4. Realistic Skin Modeling and Texturing in Facial Reconstructions (ZBrush)

17.3. Simulation of Plastic Surgery Outcomes

17.3.1. Simulation of Rhinoplasties with Modeling of Bone Structures (Rhinomodel)
17.3.2. Projection of Results in Mammoplasty and Other Body Procedures (VECTRA 3D)
17.3.3. Prediction of Changes in Post-Surgery Facial Symmetry (Geomagic Freeform)
17.3.4. Visualization of Facelift and Facelift Results (Canfield Scientific)

17.4. Scar Reduction and Skin Regeneration Simulation

17.4.1. Simulation of Dermal Regeneration in Laser Treatments (Canfield VECTRA)
17.4.2. Prediction of Scar Evolution with AI Algorithms (DermaCompare)
17.4.3. Modeling the Effects of Chemical Peels in Skin Regeneration (SkinIO)
17.4.4. Projection of Results in Advanced Healing Treatments (Medgadget SkinAI)

17.5. Projection of Results in Rejuvenation Therapies

17.5.1. Modeling the Effects of Expression Line Reduction (DeepFaceLab)
17.5.2. Simulation of Radiofrequency Therapies and Their Impact on Firmness (Visage Technologies)
17.5.3. Prediction of Results in Laser Resurfacing Procedures (Syneron Candela eTwo)
17.5.4. Visualization of the Effect of Intense Pulsed Light (IPL) Treatments (3D LifeViz)

17.6. Facial Symmetry Analysis

17.6.1. Evaluation of Facial Proportions by Means of Reference Points (Face++)
17.6.2. Real-Time Symmetry Measurement for Aesthetic Procedures (Dlib)
17.6.3. Analysis of Facial Proportions in Harmonization Procedures (MorphoStudio)
17.6.4. Comparison of Symmetry before and after Aesthetic Treatments (MediCapture)

17.7. Volume Evaluation in Body Contouring

17.7.1. Volumetric Measurement in Liposuction and Contouring Simulation (3D Sculptor)
17.7.2. Analysis of Volume Changes in Buttock Augmentation Procedures (Sculpt My Body)
17.7.3. Post-Lifting Body Contouring Evaluation (Virtual Surgical Planning)
17.7.4. Prediction of Volume Changes in Non-Invasive Body Contouring (CoolSculpting Virtual Consult)

17.8. Simulation of Hair Treatments

17.8.1. Visualization of Results in Hair Transplantation (HairMetrix)
17.8.2. Projection of Hair Growth in PRP Treatments (TruScalp AI)
17.8.3. Simulation of Hair Loss and Density in Alopecia (Keeps AI)
17.8.4. Evaluation of the Effects of Mesotherapy Treatments on Hair (HairDX)

17.9. Simulation for Body Weight Reduction

17.9.1. Projection of Results of Reductive and Shaping Treatments (Weight Loss Predictor)
17.9.2. Analysis of Body Changes in Cryolipolysis Procedures (SculpSure Consult)
17.9.3. Simulation of Volume Reduction in Ultrasonic Cavitation (UltraShape AI)
17.9.4. Visualization of Body Radiofrequency Treatment Results (InMode BodyTite)

17.10. Modeling of Liposuction Procedures

17.10.1. 3D Simulation of Abdominal Liposuction Procedure Results (VASER Shape)
17.10.2. Evaluation of Changes in Hips and Thighs after Liposuction (Body FX)
17.10.3. Modeling of Fat Reduction in Small and Targeted Areas (LipoAI)
17.10.4. Visualization of Laser-Assisted Liposuction Results (SmartLipo Triplex)

Module 18. Diagnosis and Analysis with Artificial Intelligence in Aesthetic Medicine

18.1. Diagnosis of Cutaneous Anomalies

18.1.1. Detection of Melanomas and Suspicious Skin Lesions (SkinVision)
18.1.2. Identification of Pre-Cancerous Lesions with AI Algorithms (DermaSensor)
18.1.3. Real-Time Analysis of Mole and Mole Patterns (MoleScope)
18.1.4. Classification of Skin Lesion Types with Neural Networks (SkinIO)

18.2. Skin Tone and Texture Analysis

18.2.1. Advanced Evaluation of Skin Texture Using Computer Vision (HiMirror)
18.2.2. Uniformity and Skin Tone Analysis Using AI Models (Visia Complexion Analysis)
18.2.3. Comparison of Texture Changes after Aesthetic Treatments (Canfield Reveal Imager)
18.2.4. Measurement of Firmness and Smoothness in Skin Using AI Algorithms (MySkin AI)

18.3. Detection of Sun Damage and Pigmentation

18.3.1. Identification of Hidden Sun Damage in Deep Skin Layers (VISIA Skin Analysis)
18.3.2. Segmentation and Classification of Hyperpigmentation Areas (Adobe Sensei)
18.3.3. Detection of Sunspots in Different Skin Types (SkinScope LED)
18.3.4. Evaluating the Efficacy of Treatments for Hyperpigmentation (Melanin Analyzer AI)

18.4. Diagnosis of Acne and Blemishes

18.4.1. Identification of Acne Types and Severity of Lesions (Aysa AI)
18.4.2. Classification of Acne Scars for Treatment Selection (Skinome)
18.4.3. Real-Time Analysis of Facial Blemish Patterns (Face++)
18.4.4. Evaluation of Skin Improvements after Acne Treatment (Effaclar AI)

18.5. Prediction of Skin Treatment Effectiveness

18.5.1. Modeling Skin Response to Rejuvenation Treatments (Rynkl)
18.5.2. Prediction of Results in Hyaluronic Acid Therapies (Modiface)
18.5.3. Evaluation of the Efficacy of Customized Dermatological Products (SkinCeuticals Custom D.O.S.E.)
18.5.4. Follow-Up of Skin Response in Laser Therapies (Spectra AI)

18.6. Facial Aging Analysis

18.6.1. Projection of Apparent Age and Signs of Facial Aging (PhotoAge)
18.6.2. Modeling of Skin Elasticity Loss Over Time (FaceLab)
18.6.3. Detecting Expression Lines and Deep Wrinkles in the Face (Visia Wrinkle Analysis)
18.6.4. Evaluation of the Progression of Signs of Aging (AgingBooth AI)

18.7. Detection of Vascular Skin Damage

18.7.1. Identification of Varicose Veins and Capillary Damage in the Skin (VeinViewer Vision2)
18.7.2. Evaluation of Telangiectasias and Spider Veins on the Face (Canfield Vascular Imager)
18.7.3. Analysis of the Effectiveness of Vascular Sclerosis Treatments (VascuLogic AI)
18.7.4. Follow-Up of Changes in Vascular Damage Post-Treatment (Clarity AI)

18.8. Diagnosis of Facial Volume Loss

18.8.1. Analysis of Volume Loss in Cheekbones and Facial Contours (RealSelf AI Volume Analysis)
18.8.2. Facial Fat Redistribution Modeling for Filler Planning (MirrorMe3D)
18.8.3. Tissue Density Assessment in Specific Areas of the Face (3DMDface System)
18.8.4. Simulation of Filler Results in Facial Volume Replenishment (Crisalix Volume)

18.9. Skin Elasticity and Sagging Detection

18.9.1. Measurement of Skin Elasticity and Firmness (Cutometer)
18.9.2. Analysis of Sagging in Neck and Jaw Lines (Visage Technologies Elasticity Analyzer)
18.9.3. Evaluation of Changes in Elasticity after Radiofrequency Procedures (Thermage AI)
18.9.4. Prediction of Improvement in Firmness with Ultrasound Treatments (Ultherapy AI)

18.10. Evaluation of Laser Treatment Results

18.10.1. Analysis of Skin Regeneration in Fractional Laser Therapies (Fraxel AI)
18.10.2. Monitoring of Laser Blemish and Pigmentation Removal (PicoSure AI)
18.10.3. Evaluation of Scar Reduction with Laser Therapy (CO2RE AI)
18.10.4. Comparison of Rejuvenation Results after Laser Therapy (Clear + Brilliant AI) 

Module 19. Personalization and Optimization of Aesthetic Treatments with Artificial Intelligence

19.1. Skin Care Regimen Customization

19.1.1. Skin Type Analysis and Customized Recommendations (SkinCeuticals Custom D.O.S.E)
19.1.2. Skin Sensitivity Assessment and Cosmetic Product Adjustment (Atolla)
19.1.3. Diagnosis of Aging Factors for Personalized Anti-Aging Routines (Proven Skincare)
19.1.4. Recommendations Based on Climate and Environmental Conditions (HelloAva)

19.2. Optimization of Filler and Botox Treatments

19.2.1. Simulation of Filler Results for Specific Facial Areas (Modiface)
19.2.2. Adjustment of Botox Doses in Expression Areas according to Facial Analysis (Botox Visualizer)
19.2.3. Evaluation of Duration and Effectiveness of Filler Treatments (Crisalix Botox & Filler Simulators)
19.2.4. Prediction of Results in Filler Treatments with Advanced AI (Aesthetic Immersion AI) 

19.3. Personalization of Anti-Aging Routines

19.3.1. Selection of Specific Anti-Aging Active Ingredients and Products (Function of Beauty Anti-Aging)
19.3.2. Diagnosis of Wrinkles and Fine Lines to Personalize Creams and Serums (Aysa AI)
19.3.3. Optimization of the Concentration of Active Ingredients in Anti-Aging Products (L'Oréal Perso)
19.3.4. Routine Adjustment according to the Level of Sun Exposure and Lifestyle (SkinCoach)

19.4. Development of Individualized Protocols for Peelings

19.4.1. Evaluation of Skin Sensitivity and Skin Thickness for Peels (MySkin AI)
19.4.2. Blemish and Pigmentation Analysis for Selection of Specific Peels (Canfield Reveal Imager)
19.4.3. Customization of Chemical Peels according to Skin Type (Skin IO Custom Peels)
19.4.4. Simulation of Peel Results and Regeneration Follow-Up (MoleScope AI)

19.5. Optimization of Hyperpigmentation Treatments

19.5.1. Analysis of Hyperpigmentation Causes and Selection of Appropriate Treatment (Melanin Analyzer AI)
19.5.2. Customization of Intense Pulsed Light (IPL) Blemish Treatments (Syneron Candela IPL)
19.5.3. Follow-Up of the Evolution of Hyperpigmentation after Treatment (VISIA Skin Analysis)
19.5.4. Predicting Results of Depigmentation with Advanced AI (SkinCeuticals Pigment Regulator)

19.6. Adaptation of Body Rejuvenation Treatments

19.6.1. Body Flaccidity and Firmness Analysis for Body Firming Treatments (InMode BodyTite)
19.6.2. Evaluation of Skin Tone and Texture for Skin Rejuvenation Procedures (Cutera Xeo)
19.6.3. Customization of Body Radiofrequency to Individual Needs (Thermage FLX)
19.6.4. Simulation of Results in Non-Invasive Body Rejuvenation Treatments (CoolSculpting Visualizer)

19.7. Personalization of Rosacea Treatments

19.7.1. Diagnosis of the Degree of Rosacea and Personalization of Treatment (Aysa AI for Rosacea)
19.7.2. Recommendation of Specific Products and Routines for Rosacea (La Roche-Posay Effaclar AI)
19.7.3. Adjustment of Pulsed Light Treatments to Reduce Redness (Lumenis IPL)
19.7.4. Follow-Up of Improvements and Adjustment of Protocols in Rosacea Treatment (Cutera Excel V)

19.8. Adjustment in Facial Laser Rejuvenation Protocols

19.8.1. Personalization of Fractional Laser Parameters according to Skin Type (Fraxel Dual AI)
19.8.2. Energy and Duration Optimization in Laser Resurfacing Treatments (PicoSure AI)
19.8.3. Simulation of Results and Post-Treatment Follow-Up (Clear + Brilliant)
19.8.4. Evaluation of Improvement in Texture and Tone after Laser Treatments (VISIA Complexion Analysis)

19.9. Adaptation of Body Contouring Procedures

19.9.1. Customization of Cryolipolysis Treatments in Specific Areas (CoolSculpting AI)
19.9.2. Optimization of Parameters in Focused Ultrasound Treatments (Ultherapy)
19.9.3. Fine-Tuning Body Contouring Radiofrequency Procedures (Body FX AI)
19.9.4. Simulation of Results in Non-Invasive Body Contouring (SculpSure Consult)

19.10. Personalization of Hair Regeneration Treatments

19.10.1. Evaluation of the Degree of Alopecia and Personalization of Hair Treatment (HairMetrix)
19.10.2. Optimization of Density and Growth in Hair Transplants (ARTAS iX Robotic Hair Restoration)
19.10.3. Simulation of Hair Growth in Treatments with PRP (TruScalp AI)
19.10.4. Monitoring the Response to Hair Mesotherapy Therapies (Keeps AI)

Module 20. Artificial Intelligence for Monitoring and Maintenance in Aesthetic Medicine

20.1. Post-Treatment Results Monitoring

20.1.1. Follow-Up of Evolution in Facial Treatments with Imaging (Canfield VECTRA)
20.1.2. Comparison of Before and After Results in Body Procedures (MirrorMe3D)
20.1.3. Automatic Evaluation of Texture and Tone Improvement after Treatment (VISIA Skin Analysis)
20.1.4. Documentation and Analysis of Skin Healing Progress (SkinIO)

20.2. Aesthetic Routine Adherence Analysis

20.2.1. Detection of Adherence to Daily Skin Care Routines (SkinCoach) 
20.2.2. Evaluation of Adherence to Aesthetic Product Recommendations (HelloAva)
20.2.3. Analysis of Treatment Habits and Routines according to Lifestyle (Proven Skincare)
20.2.4. Adjustment of Routines Based on Daily Adherence Follow-up (Noom Skin AI)

20.3. Detection of Early Adverse Effects

20.3.1. Identification of Adverse Reactions in Dermal Filler Treatments (SkinVision)
20.3.2. Monitoring Inflammation and Post-Treatment Redness (Effaclar AI)
20.3.3. Monitoring Side Effects after Laser Resurfacing Procedures (Fraxel AI)
20.3.4. Early Warning of Post-Inflammatory Hyperpigmentation (DermaSensor)

20.4. Long-Term Follow-Up of Facial Treatments

20.4.1. Analysis of the Durability of the Effects of Fillers and Botox (Modiface)
20.4.2. Long-Term Outcome Monitoring of Facelift Procedures (Aesthetic One) 
20.4.3. Evaluating Gradual Changes in Facial Elasticity and Firmness (Cutometer)
20.4.4. Follow-Up of Facial Volume Improvements after Fat Grafting (Crisalix Volume)

20.5. Control of Implant and Filler Results

20.5.1. Detection of Displacements or Irregularities in Facial Implants (VECTRA 3D)
20.5.2. Volume and Shape Tracking in Body Implants (3D LifeViz)
20.5.3. Analysis of the Durability of Fillers and Their Effect on Facial Contouring (RealSelf AI Volume Analysis)
20.5.4. Evaluation of Symmetry and Proportion in Facial Implants (MirrorMe3D)

20.6. Evaluation of Results in Blemish Treatments

20.6.1. Monitoring Sunspot Reduction after IPL Treatment (Lumenis AI IPL)
20.6.2. Evaluation of Changes in Hyperpigmentation and Skin Tone (VISIA Skin Analysis)
20.6.3. Monitoring the Evolution of Melasma Spots in Specific Areas (Canfield Reveal Imager)
20.6.4. Comparison of Images to Measure Effectiveness of Depigmentation Treatments (Adobe Sensei)

20.7. Skin Elasticity and Firmness Monitoring

20.7.1. Measuring Changes in Elasticity after Radiofrequency Treatments (Thermage AI)
20.7.2. Evaluation of Improvement in Firmness after Ultrasound Treatments (Ultherapy)
20.7.3. Monitoring Skin Firmness in the Face and Neck (Cutera Xeo)
20.7.4. Elasticity Monitoring after Use of Creams and Topical Products (Cutometer)

20.8. Efficiency Control in Anti-Cellulite Treatments

20.8.1. Cellulite Reduction Analysis in Cavitation Procedures (UltraShape AI)
20.8.2. Evaluation of Texture and Volume Changes after Anti-Cellulite Treatment (VASER Shape)
20.8.3. Monitoring Improvements after Body Mesotherapy Procedures (Body FX)
20.8.4. Comparison of Cellulite Reduction Results with Cryolipolysis (CoolSculpting AI)

20.9. Peel Results Stability Analysis

20.9.1. Monitoring Skin Regeneration and Texture after Chemical Peeling (VISIA Complexion Analysis) 
20.9.2. Evaluation of Sensitivity and Redness after Peels (SkinScope LED)
20.9.3. Monitoring Post-Peel Blemish Reduction (MySkin AI)
20.9.4. Comparison of Long-Term Results after Multiple Peel Sessions (VISIA Skin Analysis)

20.10. Adapting Protocols for Optimal Results

20.10.1. Adjustment of Parameters in Rejuvenation Treatments According to Results (Aesthetic One)
20.10.2. Customization of Post-Treatment Maintenance Protocols (SkinCeuticals Custom D.O.S.E)
20.10.3. Optimization of Time between Sessions of Non-Invasive Procedures (Aysa AI)
20.10.4. Home Care Recommendations Based on Treatment Response (HelloAva)

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