Digital Medicine: Bringing Digital Solutions to Medical Practice

Digital Medicine: Bringing Digital Solutions to Medical Practice

Description:
This book provides an introduction into the field of digital medicine, its wide spectrum of current clinical applications, and the future practice of medicine. With “digital health” and “digital medicine” at its core, it focuses on the combination of therapeutics with modern hard- and software solutions, including artificial intelligence and advanced diagnostic technologies such as augmented imaging and ingestible or wearable (nano)sensors, to provide best patient care. In the four parts of this book, experts in the field have authored use cases and guiding principles on the visualization of patient data analytics and clinical decision support tools, including robotic-guided interventions, as well as nursing research along with palliative and inpatient care. The book also provides examples of “digital medicine” from almost all clinical disciplines together with technical and e-learning solutions.

See more medical ebooks at here:

Digital Health: From Assumptions to Implementations

Digital Health

Artificial Intelligence for Disease Diagnosis and Prognosis in Smart Healthcare

Non-Surgical Rhinoplasty, Riccardo Nocini, Ali Pirayesh

Dose Optimization in Digital Radiography and Computed Tomography

The Thinking Healthcare System

Foreword

Our life has – over many decades – evolved into a very safe and civilized space. Still, we are confronted, ever so often, with risks, fatal accidents, unintended human errors, and seemingly unpredictable illnesses. In many areas, society has reduced these risks with dramatic success through intelligent development and engineering of iterative prevention strategies, particularly in the areas of occupational safety, air, and road transport. We have been relentlessly and constantly turning every stone at every level in order to achieve the vision and goal of zero avoidable deaths. The prominent example, Vision Zero for road traffic, which began more than 30 years ago in Sweden, is based on the premise that making mistakes from time to time is human and natural and has therefore been trying to change the prevailing conditions and systems in such a way that these mistakes do not pose a vital threat to anyone. This is done through official programs at regional, national, and European levels, in many small and large steps, all of which collectively reach enormous proportions. Airplanes hardly ever crash and being at work is statistically safer than staying at home or even in bed. Today, there is a seatbelt obligation for car drivers, it is compulsory to wear a helmet for motorcyclists, there are antilock braking systems, airbags, electronic stability control systems, and much more. When serious injuries or fatal accidents occur at a traffic junction, the situation is always investigated and subsequently translated into counteractions. For example, a roundabout is often built instead, which does not necessarily reduce the number but the seriousness of accidents. When people die frequently because of swerving on the opposite roadway, the lanes are robustly separated and the speed limit is reduced.

Why don’t we transfer this rigorousness of action to other fields and, in particular, to medicine? If people die early or otherwise from avoidable widespread diseases such as cardiovascular diseases or cancer, do we set an investigation commission to prevent the reoccurrence of such events? In fact, and unbelievably so, we do not! Instead, the way we talk about cancer nowadays, ironically resembles how we talked about accidents during the last century. “Bitter fate,” “presumably familial predisposition,” “he smoked heavily,” “she was overweight,” “was not careful,” “went too late to the doctor,” and other excuses are accentuating our shrugging and the non-action. We seem to agree with a death sentence for minor human imperfection. Or to put it in the words of the famous German singer Herbert Grönemeyer: “Wie eine träge Herde Kühe schauen wir kurz auf und grasen dann gemütlich weiter” (“Like a sluggish herd of cows, we briefly look up and then continue to graze in comfort.”)
Well-made digitization can become a vital element in obtaining a Vision Zero in medicine, which will become evident while reading the chapters of this book. Even with our current knowledge, up to half of all widespread diseases could be avoided through prevention and early detection. But as of now, we are not doing what we could and should do!

When two people in one family fall ill with the same problem, it is often not even noticed. This has two reasons: Firstly, we already know a lot about many diseases today, but we do not use knowledge of the known unknowns. If we could use digital tools to analyze genetic information or even only anamneses and case histories, then some diseases could be completely avoided, prevention individualized, and therapies improved. Secondly, we still know very little. For example, our knowledge about familial cancer is rather small because this information has not been collected systematically. Digitized medical care can, however, generate a lot of new knowledge about the many unknown unknowns. Medicine urgently needs the much-quoted, learning healthcare system.

As part of the national network on genomic medicine in Germany, many of us are currently trying to give a tangible example of what this can look like, using lung cancer as an example. There are many forms of molecular diagnosis in this disease. But we do not know much about reproducibility, and the interpretation of the results is based on the approach of the respective institution. Many colleagues are struggling to always find and realize the right therapy recommendations for their patients, not least because much is changing quickly. What we have in mind for these patients is a digitally based network at the interface between academic centers and wide-ranging care, which evaluates the quality of examinations and data from all patients: How often are specific diagnostics requested? What recommendations are made? Tools to analyze this kind of data are available. However, in Germany, data processing tools from the last century are used or not used at all.

The scenario described above for lung cancer would be a huge advancement not only for the research community but also for all patients. If we would systematically collect, although with little delay, the measures that are taken in various constellations and the results achieved in each case, we could make this information available at the point of care, making it highly relevant for patient care. However, the benefits of digitalization start much earlier than all relevant information and findings are made available in a consolidated form in one place. This is the goal of the electronic healthcare record, the EHR, called ePA in Germany. In addition, navigation applications would provide information on therapy and clinical studies for patients, or their families, as well as individual context-related information that differs with illness, condition, place of residence, and so on. Using digital health tools, patients can also get a voice themselves to deliver results and receive answers to their questions. In the right way, digitalization can democratize access to and use of healthcare. Not to mention that it probably also would be much more efficient.

Some may ask “… and data protection?” Well, what about data protection, really? The pandemic has taught us that this topic has been kept in a stranglehold of intransparent pseudo-security in Germany for too long with an “only dead data are good data” mindset. Health data must be particularly safe, no doubt. But data protection in a strict sense means patient protection. Medical data is supposed to help and protect patients, and data protection must have the same goals, without unreasonably compromising data security. So far, we have been looking at it from one side only – we have been trying to protect data optimally, creating barriers to data access and irrational constructions such as data minimization. But in case of doubt, we are also protecting the data from the patients themselves, thus leaving them unprotected from their disease. In the context of health data, patient welfare must be the utmost and mandatory guiding principle for data protection.

Data protection must also be liable for the availability of data. As long as good data protection is considered to be equivalent to the least possible processing of data, we will not make any progress. The processing and analysis of medical data can be life-prolonging, even life-saving. This, in turn, makes an understanding of data protection, which focuses on data use rather than patient use, a danger for patients. Data analysis must not only take place in the ivory towers of a few health service researchers with massive regulation and a time lag. It must take place wherever it is needed, where it helps to protect patients. We know from workshops, for instance, that cancer patients demand this. We have had this discussion time and again, and we were surprised at the beginning but it has become quite clear: The patients want their data to be used. There is a great awareness that the solidarity-based health care system in Germany enables expensive therapies and correspondingly a great willingness to give something back exists. I would even go so far as to state that it is ethically questionable not to allow such analyses of your data.

About the initial considerations concerning the Vision Zero concept, if we have a traffic light at dangerous crossroads, will we turn it off and accept traffic fatalities? But this is exactly what we are doing in the healthcare sector and it must change. This book allows the readers to know how this works. It gives a comprehensive overview of the necessary technical and organizational requirements, including impressive examples for specific implementations in prevention, diagnosis, and therapy, that shows how digital medicine can help to prevent avoidable deaths. Each chapter of this book remarkably reflects the progress made in the digitization of medicine, collectively forming the basis for Vision Zero in medicine.

Prof. Dr med. Christof von Kalle
Charité-BIH Clinical Study Center, Berlin, Germany

Table of contents :
Cover
Half Title
Title Page
Copyright Page
Table of Contents
Foreword
Acknowledgments
Introduction to Digital Medicine: Bringing Digital Solutions to Medical Practice
Part I: Digital Science
Chapter 1: Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
1.1: Machine Learning: An Overview
1.1.1: Navigating through Concepts
1.1.2: Machine Learning Approaches and Tasks
1.2: Machine Learning for Healthcare
1.2.1: Supervised Learning
1.2.2: Semi-supervised Learning
1.2.3: Unsupervised Learning
1.2.4: Reinforcement Learning
1.3: Limitations, Challenges, and Opportunities
1.3.1: Lack of Data, Labels, and Annotations
1.3.2: Learning across Domains, Tasks, and Modalities
1.3.3: Data Sharing, Privacy, and Security
1.3.4: Interpretable and Explainable Machine Learning
1.3.5: Toward Causally Informed Models
1.4: Conclusion: Quo Vadis?
Chapter 2: Data Access and Use
2.1: Legislation
2.2: Data Access
2.2.1: Governance
2.2.1.1: Roles and rights
2.2.1.2: Data access and governance process
2.2.2: Pseudonymization and Anonymization
2.2.2.1: Pseudonymization
2.2.2.2: Anonymization
2.3: Data Use
2.3.1: Storage as a Specific Use Case
2.3.1.1: Patient registries
2.3.1.2: Research databases
2.3.2: Data Sharing
Chapter 3: Data Integration
3.1: Introduction
3.2: Interoperability
3.2.1: Syntactic and Structural Interoperability
3.2.2: Semantic Interoperability
3.2.3: Process Interoperability
3.3: Extract, Transform, Load Process (ETL Process)
3.3.1: Introduction
3.3.2: Extract
3.3.3: Transform
3.3.4: Load
3.4: Data Provisioning and Data Storage
3.4.1: Data Lake
3.4.2: Data Warehouse
3.4.3: Data Provisioning “On the Fly”
3.5: Data Quality and Data Reliability
3.5.1: Definition and Relevance
3.5.2: FAIR Principles
3.6: Reintegration of Data
Chapter 4: Data Analysis in Genomic Medicine: Status, Challenges, and Developments
4.1: Introduction
4.2: Genome Sequencing in Clinical Applications
4.2.1: Read Mapping to the Human Reference Genome
4.2.1.1: Toward a complete human reference genome
4.2.2: Variant Detection
4.2.2.1: Germline variant calling
4.2.2.2: Somatic variant calling in cancer
4.2.2.3: Toward best practices for cancer somatic variant calling
4.2.3: Variant Annotation
4.2.3.1: Functional annotation
4.2.3.2: Functional consequence prediction
4.2.3.3: Population prevalence
4.2.3.4: Variant knowledge bases
4.2.3.5: Integrated annotation platforms
4.2.4: Genomic Variant Information from Primary Literature
4.2.5: Identification of Driver Mutations
4.2.6: Conclusions
Part II: Digital Health and Innovation
Chapter 5: Ethical Aspects of mHealth Technologies: Challenges and Opportunities
5.1: Introduction
5.2: Ethical Implication and Challenges
5.3: The Ontologies and Epistemologies Shaping mHealth
5.4: Concerns of Accuracy, Safety, and Security
5.5: Support for User Health Decision-Making
5.6: Protection from Physical and Mental Harm
5.7: Increasing Benefit
5.8: Intersectional Benefit and Health Justice
5.9: Conclusion
Chapter 6: i-Learning: The Next Generation e-Learning
6.1: What Is e-Learning?
6.2: History and Domains of e-Learning
6.2.1: History of Educational Technology: Teaching Machines
6.2.2: The History of Programmed Learning
6.2.3: History of Computer-Based Learning
6.2.4: History of Cybernetic Learning and Personalized Learning
6.2.5: History of Multimedia Learning
6.2.6: History of Distance Learning
6.3: The Concepts and Technical Domains of e-Learning
6.4: The Driving Forces Behind e-Learning
6.5: e-Learning in Medicine
6.6: What Comes Next?
6.6.1: Lost in Information
6.6.2: Medicine Is Increasingly Ruled by “Big Data”
6.6.3: Information: Seed of a New Age?
6.6.4: Coding: The Next Generation Literacy
6.6.5: Should Every Medical Student Learn to Program?
6.6.6: i-Learning: The Next Generation of e-Learning
6.6.7: What Drives i-Learning?
6.6.8: i-Learning and Medical Information Science (MIS)
Chapter 7: Data-Driven Nursing Research: An Overview of Underlying Concepts and Enablers
7.1: Status Quo
7.2: Nursing Data Collection for ResearchPurposes
7.2.1: Nursing Minimum Data Sets
7.2.2: Nursing Sensitive Indicators
7.3: Interoperability of Data Exchange Across Different Systems and Facilities
7.3.1: Semantic Interoperability
7.3.2: Syntactic Interoperability
7.3.3: Inter-institutional Data Transfer
7.4: Discussion
Chapter 8: Designing the Hospital of the Future: A Framework to Guide Digital Innovation
8.1: Sharpening the Vision
8.2: The Conceptual HoF Framework
8.2.1: Patient (P)
8.2.2: Staff (S)
8.2.3: Treatment and Intervention (T)
8.2.4: Logistics and Supply (L)
8.2.5: Management and Organization (M)
8.2.6: Data and Control (D)
8.2.7: Infrastructure (I)
8.3: Evaluation of HoF Dimensions
8.4: Enablers
8.5: The Four-Step Approach
Chapter 9: Combining Digital Medicine, Innovative Geospatial and Environmental Data in Environmental Health Sciences to Create Sustainable Health
9.1: Introduction and Setting the Stage
9.2: The Exposome
9.3: Environmental Health Services
9.4: Data for Environmental Health Services
9.5: Measuring Exposomes by Personal Samples, Individual Exposome
9.6: The Role of Earth Observation Data in Public Health Research
9.7: Imitating Natural Exposure Conditions in Human Exposure Chambers
9.8: Challenges of Digital Medicine in Environmental Health
9.9: Future Prospects
Chapter 10: Digitalization and (Nano)Robotics in Nanomedicine
10.1: Introduction
10.2: Imaging in Nanomedicine
10.3: Simulations in Nanomedicine
10.4: Robotics and Nano- and Microbots in Biomedicine
Part III: Digital Diagnostics
Chapter 11: Neural Networks in Molecular Imaging: Theory and Applications
11.1: Introduction
11.2: Theory
11.2.1: Machine Learning
11.2.2: Neural Networks
11.3: Applications
11.3.1: Alzheimer’s Disease
11.3.2: Lung Cancer
11.4: Outlook
Chapter 12: Precision Oncology: Molecular Diversification of Tumor Patients
12.1: Introduction
12.2: Genomics
12.2.1: Short Nucleotide Variants and Structural Variants
12.2.2: Copy Number Alterations
12.2.3: Homologous Recombination Repair and Deficiency
12.2.4: Tumor Mutational Burden
12.2.5: Mutational Signatures
12.2.6: Germline Variants
12.3: Single-Cell Sequencing
12.4: Liquid Biopsy
12.5: Data Integration and Molecular Tumor Boards
12.5.1: Data Integration
12.5.2: Standard Nomenclature
12.6: Conclusion
Chapter 13: Digital Applications in Precision Pathology
13.1: Introduction
13.2: Precision Pathology
13.2.1: Machine Learning
13.2.2: Machine Intelligence
13.3: Computational, Algebraic, and Encoded Pathology
13.4: Applications of Precision Pathology
13.5: Digital Image and Data Analysis
Chapter 14: Computational Pathology
14.1: Introduction
14.2: AI in Pathology is a New Field
14.3: Impact on Clinical Routine
14.4: Impact on Research
14.5: Structured Reports Are Essential for AI Development
14.6: Datasets at Scale via Federated Learning
14.7: Outlook
Chapter 15: Digital Neuropathology
15.1: Introduction: The Roots of Neuropathology
15.2: Traditional Histology in Future Lights
15.3: Advanced Neuro-oncologic Diagnostics
15.4: In Situ Microscopy in Real Time
15.5: Conclusion
Chapter 16: Application of Artificial Intelligence in Gastrointestinal Endoscopy
16.1: Introduction
16.2: Esophagus
16.3: Stomach
16.4: Small Intestine
16.5: Colon
16.6: Computer-Aided Detection of Polyps
16.7: Computer-Aided Characterization (CADX) of Polyps
16.8: Conclusion
Part IV: Digital Therapeutics
Chapter 17: Digital Transformation Processes in Acute Inpatient Care in Germany
17.1: Introduction
17.2: Approaches to Implementation
17.2.1: Top-Down
17.2.2: Bottom-Up
17.2.3: The Holistic Approach
17.3: Direct Care Perspective
17.3.1: Digitalization in the Context of Care Relief
17.4: Care Management Perspective
17.4.1: Sector-Specific Challenges
17.4.1.1: Structural difficulties
17.4.1.2: Professional difficulties
17.4.2: Communication in the Care Team
17.4.3: Qualification of Employees
17.5: Perspective of Nursing Science
17.5.1: The Evaluability of Data: New Ways and Opportunities for Care
17.5.2: Nursing Research
17.5.2.1: Nursing indicators as a clinical and cross-sectoral management tool
17.5.2.2: Imagine the given scenario
17.6: Prospects
Chapter 18: Digital Medicine in Neurology
18.1: Introduction
18.2: Multiple Sclerosis and Parkinson’s Disease
18.2.1: Multiple Sclerosis
18.2.1.1: Monitoring MS symptoms and disease course
18.2.1.2: Digital treatment support
18.2.1.3: Prediction of disease activity and treatment decisions
18.2.2: Parkinson’s Disease
18.2.2.1: Monitoring of symptoms and activities of daily living
18.2.2.2: Digital treatment support
18.2.2.3: Automated diagnosis, prediction of disease severity, and treatment decisions
18.3: Summary and Outlook
Chapter 19: Neurorehabilitation Medicine
19.1: Introduction
19.2: Therapeutic Potential of Digital Medicine in Neurorehabilitation
19.2.1: Virtual Reality Rehabilitation
19.2.2: Telerehabilitation
19.2.3: Robotic Therapy in Neurorehabilitation
19.2.4: Humanoid Robot Assistance in Neurorehabilitation
19.3: Diagnostic and Prognostic Potential of Digital Medicine in Neurorehabilitation
19.4: Digital Medicine as a Long-Term Medical Aid in and after Neurorehabilitation
19.5: Conclusion
Chapter 20: Digital Psychiatry
20.1: Entering a New World of Mental Health Care
20.2: Diagnosis and Prevention, Prognosis, and Treatment Selection
20.2.1: Diagnosis and Prevention
20.2.2: Prognosis, Treatment Selection, and Outcome Prediction
20.3: Digital Treatment Options for Mental Health
20.3.1: Psychotherapy at a Distance (Text-Based and Video-Based Treatment)
20.3.2: Internet- and Mobile-Based Psychological Interventions
20.3.2.1: Types
20.3.2.2: Evidence
20.3.2.3: Quality assurance
20.4: Limitations and Challenges for the New World of Mental Health
20.4.1: Efficacy vs. Effectiveness
20.4.2: Data Safety and Legal Concerns
20.4.3: Ethical Challenges
20.5: Conclu
Chapter 21: Digital Neurosurgery
21.1: Introduction
21.2: Intraoperative Imaging
21.2.1: Intraoperative Ultrasound
21.2.2: Intraoperative Computed Tomography
21.2.3: Intraoperative Magnetic Resonance Imaging
21.3: Neuronavigation
21.3.1: Intraoperative Navigation: Brain
21.3.2: Intraoperative Navigation: Spine
21.3.3: Hybrid Operating Rooms
21.4: Robotics
21.4.1: Augmented Reality
Chapter 22: Digital Surgery: The Convergence of Robotics, Artificial Intelligence, and Big Data
22.1: Introduction
22.2: State-of-the-Art Surgery
22.3: Digital Infrastructure in the Surgical Environment
22.3.1: The Modern Operating Room
22.3.2: Preoperative Planning and Intraoperative Decision-Making
22.3.3: Documentation and Reporting
22.3.4: Surgical Education and Training
22.4: Digital Revolution of Surgery
22.4.1: Surgical Robotics
22.4.1.1: Visualization and cognition
22.4.1.2: Advanced instruments
22.4.1.3: Autonomous robotic systems
22.4.2: Artificial Intelligence
22.4.2.1: Machine learning
22.4.2.2: Natural language processing
22.4.2.3: Artificial neural networks
22.4.2.4: Computer vision
22.4.3: Big Data
22.5: Problems and Challenges
22.5.1: Technical Infrastructure and Interoperability
22.5.2: Technical Expertise
22.6: Future of Surgery
22.6.1: The Need for Interprofessional Teams
22.6.2: The Future Role of Surgeons
Chapter 23: Digital Urology
23.1: Introduction
23.2: Telemedicine
23.3: Robotics
23.3.1: Robot-Assisted Surgery
23.3.2: Radiosurgery
23.4: Artificial Intelligence
23.5: Conclusion
Chapter 24: Digitalization in Anesthesiology and Intensive Care
24.1: Introduction
24.2: The Perioperative Process: Digitalization in Anesthesiology and Critical Care
24.3: Digital Anesthesiology and Intensive Care in Research and Education
24.4: Fair Anesthesia: Data Sharing and Open Science
24.5: Clinical Decision Support
24.6: Perspective and Vision
Chapter 25: Digital Palliative Care
25.1: Introduction
25.2: Integration of Digital Palliative Care
25.2.1: Early Integration/Advance Care Planning
25.2.2: Symptom Control in the Context of Palliative Care Treatment
25.2.2.1: PROMs, the basis of patient-centered medicine
25.2.3: Patient Care/Management/Limits and Opportunities
25.2.4: Cross-Sectoral Care
25.2.5: Digital Bereavement Counseling/Settlement of the Digital Estate
25.3: Research in the Field of Digital Palliative Care
25.4: Education
25.5: Prospects
Chapter 26: Digital Medicine in Pulmonary Medicine
26.1: Introduction
26.2: Overview of the Applications Mentioned on the Homepage of the Deutsche Atemwegsliga e.V.
26.2.1: Kaia COPD App
26.2.2: Atemwege Gemeinsam Gehen App/Breath Walk Together App
26.2.3: OMROM Asthma Diary App
26.2.4: Vivatmo App
26.2.5: breaszyTrack – dein Asthma-Helfer App/breaszyTrack – Your Asthma Helper App
26.2.6: www.copd-aktuell.de is an Online Portal
26.2.7: “Kata – Deine Inhalationshilfe für die Anwendung Eines Dosieraerosols” App/“Kata – Your Inhalations Aid for the Use of a Metered Dose Inhaler” App
26.2.8: “myAir” App
26.2.9: “Nichtraucher Helden” App/“Non-Smoking Heroes” App
26.2.10: “SaniQ Asthma!” App
26.2.11: “Therakey” is a COPD Online Portal
26.3: Description of the Functioning of the SaniQ App
26.4: What Studies Have Already Been Conducted with This Application?
26.4.1: Rhineland-Palatinate Breathes Through: Telemedicine for Healthy Lungs
26.4.2: Experiences With Digital Care for Patients With Chronic and Acute Lung Diseases During the SARS-CoV-2 Pandemic
26.4.3: COVID-19@Home: App-Based Telemonitoring in the GP Practice
26.5: What Functions Should a Telemedical Application Fulfill in the Future? A Concept for a Digital Supply
26.5.1: Description of Telemedicine
26.5.2: Communication
26.5.3: Medication
26.5.4: Monitoring
26.5.5: Interfaces
26.5.6: Extensions
26.5.7: Conclusion
Chapter 27: Digital Rheumatology
27.1: Introduction
27.2: Acceleration of Diagnosis
27.2.1: Status Quo: Shortage of Rheumatologists and Diagnostic Odyssey
27.2.2: Diagnostic Decision Support Systems
27.3: Personalized Disease Monitoring
27.3.1: Video Consultations
27.3.2: Electronic Patient-Reported Outcomes
27.3.3: Wearables and Smartphones
27.3.4: Self-Sampling
27.4: Digital Therapy
27.4.1: Patient Education
27.4.2: Digital Therapy for Rheumatic Complaints
27.4.3: Digital Therapy for Comorbidities
27.5: Artificial Intelligence in Rheumatology
27.6: Limitations and Potential of Digital Rheumatology
27.6.1: Limitations
27.6.2: Potential
Chapter 28: Digital Dermatology
28.1: Introduction
28.2: AI-Supported Image Analysis in Dermatology
28.3: Teledermatology
28.4: Smart Skin and Wearables
28.5: Digital Dermatopathology
28.6: Digital Teaching
28.7: Summary
Chapter 29: Digital Neonatology
29.1: Introduction
29.2: Challenges
29.3: Delivery Room Management
29.4: Defining Diseases
29.5: Medication and Nutrition
29.6: Monitoring, Event Detection, and Automated Therapy
29.7: Bacterial Infections
29.8: Neurology and Neurodevelopment
29.9: Conclusion
Chapter 30: Digital Medicine and Artificial Intelligence in the Area of Breast and Gynecologic Cancer
30.1: Introduction
30.2: AI in the Special Field of Gynecologic Oncology
30.3: AI in the Field of Breast Cancer
30.4: AI and Gynecologic Cancers
30.5: Cervical Cancer
30.6: Ovarian Cancer
30.7: Endometrial Carcinoma
30.8: Summary and Visions
Chapter 31: Digital Otorhinolaryngology (Ear-Nose-Throat)
31.1: Introduction
31.2: Surgical Planning Using Virtual Reality Systems
31.3: Intraoperative 4K and 3D Visualization, Navigation Systems, and Navigated Instruments
31.3.1: Intraoperative 4K and 3D Visualization
31.3.2: Navigation Systems and Navigated Instruments
31.4: Robotics in Head and Neck Surgery
31.4.1: Robotic Systems and Their Application in Head and Neck Surgery
31.5: Future Perspectives
31.6: Digital Ear Surgery: Intraoperative Live Imaging of Electrocochleography During Cochlear Implant Surgery
Chapter 32: Digital Orthopedics and Traumatology
32.1: Introduction
32.2: Processes and Divisions
32.2.1: Telemedicine
32.2.2: Pre- and Postoperative Processes
32.2.3: Intraoperative Digitization
32.2.4: Education and Teaching
32.2.5: Ethical and Legal Framework
32.3: Outlook and Potential Next Steps
32.3.1: Information on Two Columns
32.3.2: Teaching and Training
32.3.3: Budget and Legal Support for Clinics and Outpatients
Index