A program that will train you in the field of biomedical image analysis and the control of socio-health data, with the goal of optimizing medical care"

One of the most outstanding advantages that biomedical imaging offers to the clinical branch is to minimize surgical intervention in patients. This will not only improve medical processes in the area of surgery, but will also protect those affected who, due to parallel problems, cannot be operated on. Additionally, the incorporation of Big Data has made it possible to contrast heterogeneous information from different clinical centers, which has been very useful on a global level with COVID. Given the growing demand in the health care labor market for professionals who can adapt to new advances and know how to manage changes in primary and secondary care, specialists have been faced with the need to extend their field of action towards telemedicine.

In response to this professional demand, TECHhas developed a comprehensive program in Biomedical Image Analysis and Big Data in E-Health aimed at graduates in Nursing. In this way, students who receive the program will have a Relearning methodology that will avoid long hours of study and will enable them to assimilate the concepts in a simple and progressive way. 

TECHhas also called on a team of experts who will not only transmit the theoretical knowledge of this program to the graduates, but will also be able to share with them their experiences in the sector and the real scenario of action. Thanks to their collaboration, students will have at their disposal a direct communication channel through which they will be able to solve all their doubts regarding the syllabus. This is a brand new academic experience for professionals who are looking for excellence and adapted instruction with experts in telemedicine.  

Sign up to learn about the advantages of nano-robots in identifying and fighting cancer cells"

This Postgraduate diploma in Biomedical Image Analysis and Big Data in E-Health contains the most complete and up-to-date scientific program on the market. The most important features include:

• The development of practical cases presented by experts in biomedical imaging and databases 
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice 
• The practical exercises where the self-evaluation 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

Thanks to TECH, you will learn more about in radiology and the tools such as SPECT and PET that intervene in medicine"

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

Its multimedia content, developed with the latest educational technology, will allow the professional a situated and contextual learning, that is, a simulated environment that will provide an immersive training programmed to train in real situations.

The design of this program focuses on Problem-Based Learning, in which the professional will have to try to solve the different professional practice situations that will arise throughout the academic course. For this purpose, the student will be assisted by an innovative interactive video system created by renowned experts.

Delve into the telemedicine paradigm and understand the benefits in the care of patients with infectious diseases"

Delve into the intricacies of Big Data in public health to address risk prediction and personalized medicine"

This Postgraduate diploma has been developed jointly with a professional team specialized in the health area, with years of experience in the clinical scenario. It is a program that proposes the simulation of real cases, so that students know how to act in professional practice with the guidance of experts. Additionally, students have 450 hours of theoretical-practical and additional material to make their studies more dynamic. All this has been applied in this program, 100% online so that, in only 6 months, the Nursing specialist can develop their knowledge, being able to work on updating his professional skills. Additionally, TECH applies the Relearning methodology, so that students assimilate the knowledge gradually and do not have to invest long hours of memorization in the subject. 

Explore the techniques, recognition and intervention through biomedical imaging, thanks to TECH”

Module 1. Techniques, Recognition and Intervention using Biomedical Imaging

1.1. Medical Imaging

1.1.1. Modalities in Medical Imaging
1.1.2. Objectives in Medical Imaging Systems
1.1.3. Medical Imaging Storage Systems

1.2. Radiology

1.2.1. Imaging Method
1.2.2. Radiology Interpretation
1.2.3. Clinical Applications

1.3. Computed Tomography (CT) 

1.3.1. Principle of Operation
1.3.2. Image Generation and Acquisition
1.3.3. Computerized Tomography. Typology
1.3.4. Clinical Applications

1.4. Magnetic Resonance Imaging (MRI)

1.4.1. Principle of Operation
1.4.2. Image Generation and Acquisition
1.4.3. Clinical Applications

1.5. Ultrasound: Ultrasound and Doppler Sonography

1.5.1. Principle of Operation
1.5.2. Image Generation and Acquisition 
1.5.3. Typology
1.5.4. Clinical Applications

1.6. Nuclear medicine

1.6.1. Physiological Basis in Nuclear Studies. Radiopharmaceuticals and Nuclear Medicine
1.6.2. Image Generation and Acquisition
1.6.3. Types of Tests
1.6.4. Principles and Fundamentals of Executive Functions

1.6.3.1. Gammagraphy
1.6.3.2. SPECT
1.6.3.3. PET:
1.6.3.4. Clinical Applications

1.7. Image-Guided Interventions

1.7.1. Interventional Radiology
1.7.2. Interventional Radiology Objectives
1.7.3. Procedures
1.7.4. Advantages and Disadvantages

1.8. Image Quality

1.8.1. Technique
1.8.2. Contrast 
1.8.3. Resolution
1.8.4. Noise
1.8.5. Distortion and Artifacts

1.9. Medical Imaging Tests. Biomedicine

1.9.1. Creating 3D Images
1.9.2. Biomodels

1.9.2.1. DICOM Standard
1.9.2.2. Clinical Applications

1.10. Radiological Protection 

1.10.1. European Legislation Applicable to Radiology Services
1.10.2. Safety and Action Protocols
1.10.3. Radiological Waste Management
1.10.4. Radiological Protection
1.10.5. Care and Characteristics of Rooms

Module 2. Big Data in Medicine: Massive Medical Data Processing

2.1. Big Data in Biomedical Research

2.1.1. Data Generation in Biomedicine
2.1.2. High-Throughput Technology 
2.1.3. Uses of High-Throughput Data. Hypotheses in the Age of Big Data

2.2. Data Pre-Processing in Big Data

2.2.1. Data Pre-Processing
2.2.2. Methods and Approaches 
2.2.3. Problems with Data Pre-Processing in Big Data

2.3. Structural Genomics 

2.3.1. Sequencing the Human Genome
2.3.2. Sequencing vs. Chips
2.3.3. Variant Discovery

2.4. Functional Genomics

2.4.1. Functional Notation
2.4.2. Mutation Risk Predictors
2.4.3. Association Studies in Genomics

2.5. Transcriptomics 

2.5.1. Techniques to Obtain Massive Data in Transcriptomics: RNA-seq
2.5.2. Data Normalization in Transcriptomics
2.5.3. Differential Expression Studies

2.6. Interactomics and Epigenomics

2.6.1. The Role of Cromatine in Gene Expression
2.6.2. High-Throughput Studies in Interactomics
2.6.3. High-Throughput Studies in Epigenetics

2.7. Proteomics

2.7.1. Analysis of Mass Spectrometry Data
2.7.2. Post-Translational Modifications Study
2.7.3. Quantitative Proteomics

2.8. Enrichment and Clustering Techniques

2.8.1. Contextualizing Results
2.8.2. Clustering Algorithms in Omics Techniques
2.8.3. Repositories for Enrichment: Gene Ontology and KEGG

2.9. Applying Big Data to Public Health

2.9.1. Discovery of New Biomarkers and Therapeutic Targets
2.9.2. Risk Predictors
2.9.3. Personalized Medicine

2.10. Big Data Applied to Medicine

2.10.1. Potential for Diagnostic and Preventive Assistance
2.10.2. Use of Machine Learning Algorithms in Public Health
2.10.3. The Problem of Privacy

Module 3. Applications of Artificial Intelligence and the Internet of Things (IoT) in Telemedicine

3.1. eHealth Platforms: Personalizing Healthcare Services

3.1.1. e-Health Platforms:
3.1.2. Resources for e-Health Platforms
3.1.3. Digital Europe Program. Digital Europe-4-Health and Horizon Europe

3.2. Artificial Intelligence in Healthcare I: New Solutions in Computer Applications

3.2.1. Remote Analysis of Results
3.2.2. Chatbox
3.2.3. Prevention and Real-Time Monitoring
3.2.4. Preventive and Personalized Medicine in Oncology

3.3. Artificial Intelligence in Healthcare II:

3.3.1. Monitoring Patients with Reduced Mobility
3.3.2. Cardiac Monitoring, Diabetes, Asthma
3.3.3. Health and Wellness Apps

3.3.3.1. Heart Rate Monitors
3.3.3.2. Blood Pressure Bracelets

3.3.4. Ethical Use of AI in the Medical Field. Data Protection

3.4. Artificial Intelligence Algorithms for Image Processing

3.4.1. Artificial Intelligence Algorithms for Image Handling
3.4.2. Image Diagnosis and Monitoring in Telemedicine

3.4.2.1. Melanoma Diagnosis

3.4.3. Limitations and Challenges in Image Processing in Telemedicine

3.5. Application Acceleration using Graphics Processing Units (GPU) in Medicine

3.5.1. Program Parallelization
3.5.2. GPU Operations
3.5.3. Application Acceleration using GPU in Medicine

3.6. Natural Language Processing (NLP) in Telemedicine

3.6.1. Text Processing in the Medical Field. Methodology
3.6.2. Natural Language Processing in Therapy and Medical Records
3.6.3. Limitations and Challenges in Natural Language Processing in Telemedicine

3.7. The Internet of Things (IoT) in Telemedicine. Applications

3.7.1. Monitoring Vital Signs. Wearables

3.7.1.1. Blood Pressure, Temperature, and Heart Rate

3.7.2. The IoT and Cloud Technology

3.7.2.1. Data Transmission to the Cloud 

3.7.3. Self-Service Terminals

3.8. IoT in Patient Monitoring and Care

3.8.1. IoT Applications for Emergency Detection
3.8.2. The Internet of Things in Patient Rehabilitation
3.8.3. Artificial Intelligence Support in Victim Recognition and Rescue

3.9. Nano-Robots. Typology

3.9.1. Nanotechnology
3.9.2. Types of Nano-Robots

3.9.2.1. Assemblers. Applications
3.9.2.2. Self-Replicating. Applications

3.10. Artificial Intelligence in COVID-19 Control

3.10.1. COVID-19 and Telemedicine
3.10.2. Management and Communication of Breakthroughs and Outbreaks
3.10.3. Outbreak Prediction in Artif

A program designed for professionals like you, who wish to project their professional career towards future trends in nano-robots”