Deep Learning for Targeted Treatments

Rishabha Malviya, PhD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients. He has authored more than 150 research/review papers for national/international journals of repute. He has been granted more than 10 patents from different countries while a further 40 patents are published/under evaluation. Gheorghita Ghinea, PhD, is a professor in Computing, Department of Computer Science Brunel University London. His research activities lie at the confluence of computer science, media, and psychology, and particularly interested in building semantically underpinned human-centered e-systems, particularly integrating human perceptual requirements. Has published more than 30+ articles and received 10+ research grants. Rajesh Kumar Dhanaraj, PhD, is an associate professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed 20+ books on various technologies and 35+ articles and papers in various refereed journals and international conferences and contributed chapters to the books. His research interests include machine learning, cyber-physical systems, and wireless sensor networks. He is an Expert Advisory Panel Member of Texas Instruments Inc USA. Balamurugan Balusamy, PhD, is a professor at Galgotias University. He has published 30+ books on various technologies as well as more than 150 journal articles, conferences, and book chapters. Sonali Sundram completed B. Pharm & M. Pharm (pharmacology) from AKTU, Lucknow, and is working at Galgotias University, Greater Noida. Her areas of interest are neurodegeneration, clinical research, and artificial intelligence. She has more than 8 patents to her credit.

• Preface xviiAcknowledgement xix1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science 1Dhanalekshmi Unnikrishnan Meenakshi, Selvasudha Nandakumar, Arul Prakash Francis, Pushpa Sweety, Shivkanya Fuloria, Neeraj Kumar Fuloria, Vetriselvan Subramaniyan and Shah Alam Khan1.1 Introduction 21.2 Drug Discovery, Screening and Repurposing 51.3 DL and Pharmaceutical Formulation Strategy 111.3.1 DL in Dose and Formulation Prediction 111.3.2 DL in Dissolution and Release Studies 151.3.3 DL in the Manufacturing Process 161.4 Deep Learning Models for Nanoparticle-Based Drug Delivery 191.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory 201.4.2 Artificial Intelligence and Drug Delivery Algorithms 211.4.3 Nanoinformatics 221.5 Model Prediction for Site-Specific Drug Delivery 231.5.1 Prediction of Mode and a Site-Specific Action 231.5.2 Precision Medicine 261.6 Future Scope and Challenges 271.7 Conclusion 29References 302 Role of Deep Learning, Blockchain and Internet of Things in Patient Care 39Akanksha Sharma, Rishabha Malviya and Sonali Sundram2.1 Introduction 402.2 IoT and WBAN in Healthcare Systems 422.2.1 IoT in Healthcare 422.2.2 WBAN 442.2.2.1 Key Features of Medical Networks in the Wireless Body Area 442.2.2.2 Data Transmission & Storage Health 452.2.2.3 Privacy and Security Concerns in Big Data 452.3 Blockchain Technology in Healthcare 462.3.1 Importance of Blockchain 462.3.2 Role of Blockchain in Healthcare 472.3.3 Benefits of Blockchain in Healthcare Applications 482.3.4 Elements of Blockchain 492.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling 512.3.6 Mobile Health and Remote Monitoring 532.3.7 Different Mobile Health Application with Description of Usage in Area of Application 542.3.8 Patient-Centered Blockchain Mode 552.3.9 Electronic Medical Record 572.3.9.1 The Most Significant Barriers to Adoption Are 602.3.9.2 Concern Regarding Negative Unintended Consequences of Technology 602.4 Deep Learning in Healthcare 622.4.1 Deep Learning Models 632.4.1.1 Recurrent Neural Networks (RNN) 632.4.1.2 Convolutional Neural Networks (CNN) 642.4.1.3 Deep Belief Network (DBN) 652.4.1.4 Contrasts Between Models 662.4.1.5 Use of Deep Learning in Healthcare 662.5 Conclusion 702.6 Acknowledgments 70References 703 Deep Learning on Site-Specific Drug Delivery System 77Prem Shankar Mishra, Rakhi Mishra and Rupa Mazumder3.1 Introduction 783.2 Deep Learning 813.2.1 Types of Algorithms Used in Deep Learning 813.2.1.1 Convolutional Neural Networks (CNNs) 823.2.1.2 Long Short-Term Memory Networks (LSTMs) 833.2.1.3 Recurrent Neural Networks 833.2.1.4 Generative Adversarial Networks (GANs) 843.2.1.5 Radial Basis Function Networks 843.2.1.6 Multilayer Perceptron 853.2.1.7 Self-Organizing Maps 853.2.1.8 Deep Belief Networks 853.3 Machine Learning and Deep Learning Comparison 863.4 Applications of Deep Learning in Drug Delivery System 873.5 Conclusion 90References 904 Deep Learning Advancements in Target Delivery 101Sudhanshu Mishra, Palak Gupta, Smriti Ojha, Vijay Sharma, Vicky Anthony and Disha Sharma4.1 Introduction: Deep Learning and Targeted Drug Delivery 1024.2 Different Models/Approaches of Deep Learning and Targeting Drug 1044.3 QSAR Model 1054.3.1 Model of Deep Long-Term Short-Term Memory 1054.3.2 RNN Model 1074.3.3 CNN Model 1084.4 Deep Learning Process Applications in Pharmaceutical 1094.5 Techniques for Predicting Pharmacotherapy 1094.6 Approach to Diagnosis 1104.7 Application 1134.7.1 Deep Learning in Drug Discovery 1144.7.2 Medical Imaging and Deep Learning Process 1154.7.3 Deep Learning in Diagnostic and Screening 1164.7.4 Clinical Trials Using Deep Learning Models 1164.7.5 Learning for Personalized Medicine 1174.8 Conclusion 121Acknowledgment 122References 1225 Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors 127Selvasudha Nandakumar, Shah Alam Khan, Poovi Ganesan, Pushpa Sweety, Arul Prakash Francis, Mahendran Sekar, Rukkumani Rajagopalan and Dhanalekshmi Unnikrishnan Meenakshi5.1 Introduction 1285.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis 1325.2.1 Gene Identification and Genome Data 1335.2.2 Image Diagnosis 1355.2.3 Radiomics, Radiogenomics, and Digital Biopsy 1375.2.4 Medical Image Analysis in Mammography 1385.2.5 Magnetic Resonance Imaging 1395.2.6 CT Imaging 1405.3 dl in Next-Generation Sequencing, Biomarkers, and Clinical Validation 1415.3.1 Next-Generation Sequencing 1415.3.2 Biomarkers and Clinical Validation 1425.4 dl and Translational Oncology 1445.4.1 Prediction 1445.4.2 Segmentation 1465.4.3 Knowledge Graphs and Cancer Drug Repurposing 1475.4.4 Automated Treatment Planning 1495.4.5 Clinical Benefits 1505.5 DL in Clinical Trials—A Necessary Paradigm Shift 1525.6 Challenges and Limitations 1555.7 Conclusion 157References 1576 Personalized Therapy Using Deep Learning Advances 171Nishant Gaur, Rashmi Dharwadkar and Jinsu Thomas6.1 Introduction 1726.2 Deep Learning 1746.2.1 Convolutional Neural Networks 1756.2.2 Autoencoders 1806.2.3 Deep Belief Network (DBN) 1826.2.4 Deep Reinforcement Learning 1846.2.5 Generative Adversarial Network 1866.2.6 Long Short-Term Memory Networks 188References 1917 Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework 199Swati Verma, Rishabha Malviya, Md Aftab Alam and Bhuneshwar Dutta Tripathi7.1 Introduction 2007.2 Artificial Intelligence 2007.2.1 Types of Artificial Intelligence 2017.2.1.1 Machine Intelligence 2017.2.1.2 Types of Machine Intelligence 2037.2.2 Applications of Artificial Intelligence 2047.2.2.1 Role in Healthcare Diagnostics 2057.2.2.2 AI in Telehealth 2057.2.2.3 Role in Structural Health Monitoring 2057.2.2.4 Role in Remote Medicare Management 2067.2.2.5 Predictive Analysis Using Big Data 2077.2.2.6 AI’s Role in Virtual Monitoring of Patients 2087.2.2.7 Functions of Devices 2087.2.2.8 Clinical Outcomes Through Remote Patient Monitoring 2107.2.2.9 Clinical Decision Support 2117.2.3 Utilization of Artificial Intelligence in Telemedicine 2117.2.3.1 Artificial Intelligence–Assisted Telemedicine 2127.2.3.2 Telehealth and New Care Models 2137.2.3.3 Strategy of Telecare Domain 2147.2.3.4 Role of AI-Assisted Telemedicine in Various Domains 2167.3 AI-Enabled Telehealth: Social and Ethical Considerations 2187.4 Conclusion 219References 2208 Deep Learning Framework for Cancer Diagnosis and Treatment 229Shiv Bahadur and Prashant Kumar8.1 Deep Learning: An Emerging Field for Cancer Management 2308.2 Deep Learning Framework in Diagnosis and Treatment of Cancer 2328.3 Applications of Deep Learning in Cancer Diagnosis 2338.3.1 Medical Imaging Through Artificial Intelligence 2348.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning 2348.3.3 Digital Pathology Through Deep Learning 2358.3.4 Application of Artificial Intelligence in Surgery 2368.3.5 Histopathological Images Using Deep Learning 2378.3.6 MRI and Ultrasound Images Through Deep Learning 2378.4 Clinical Applications of Deep Learning in the Management of Cancer 2388.5 Ethical Considerations in Deep Learning–Based Robotic Therapy 2398.6 Conclusion 240Acknowledgments 240References 2419 Applications of Deep Learning in Radiation Therapy 247Akanksha Sharma, Ashish Verma, Rishabha Malviya and Shalini Yadav9.1 Introduction 2489.2 History of Radiotherapy 2509.3 Principal of Radiotherapy 2519.4 Deep Learning 2519.5 Radiation Therapy Techniques 2549.5.1 External Beam Radiation Therapy 2579.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) 2599.5.3 Intensity Modulated Radiation Therapy (IMRT) 2609.5.4 Image-Guided Radiation Therapy (IGRT) 2619.5.5 Intraoperative Radiation Therapy (IORT) 2639.5.6 Brachytherapy 2659.5.7 Stereotactic Radiosurgery (SRS) 2689.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist 2699.6.1 Deep Learning in Patient Assessment 2699.6.1.1 Radiotherapy Results Prediction 2699.6.1.2 Respiratory Signal Prediction 2719.6.2 Simulation Computed Tomography 2719.6.3 Targets and Organs-at-Risk Segmentation 2739.6.4 Treatment Planning 2749.6.4.1 Beam Angle Optimization 2749.6.4.2 Dose Prediction 2769.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists 2779.7 Conclusion 280References 28110 Application of Deep Learning in Radiation Therapy 289Shilpa Rawat, Shilpa Singh, Md. Aftab Alam and Rishabha Malviya10.1 Introduction 29010.2 Radiotherapy 29110.3 Principle of Deep Learning and Machine Learning 29310.3.1 Deep Neural Networks (DNN) 29410.3.2 Convolutional Neural Network 29510.4 Role of AI and Deep Learning in Radiation Therapy 29510.5 Platforms for Deep Learning and Tools for Radiotherapy 29710.6 Radiation Therapy Implementation in Deep Learning 30010.6.1 Deep Learning and Imaging Techniques 30110.6.2 Image Segmentation 30110.6.3 Lesion Segmentation 30210.6.4 Computer-Aided Diagnosis 30210.6.5 Computer-Aided Detection 30310.6.6 Quality Assurance 30410.6.7 Treatment Planning 30510.6.8 Treatment Delivery 30510.6.9 Response to Treatment 30610.7 Prediction of Outcomes 30710.7.1 Toxicity 30910.7.2 Survival and the Ability to Respond 31010.8 Deep Learning in Conjunction With Radiomoic 31210.9 Planning for Treatment 31410.9.1 Optimization of Beam Angle 31510.9.2 Prediction of Dose 31510.10 Deep Learning’s Challenges and Future Potential 31610.11 Conclusion 317References 31811 Deep Learning Framework for Cancer 333Pratishtha11.1 Introduction 33411.2 Brief History of Deep Learning 33511.3 Types of Deep Learning Methods 33611.4 Applications of Deep Learning 33911.4.1 Toxicity Detection for Different Chemical Structures 33911.4.2 Mitosis Detection 34011.4.3 Radiology or Medical Imaging 34111.4.4 Hallucination 34211.4.5 Next-Generation Sequencing (NGS) 34211.4.6 Drug Discovery 34311.4.7 Sequence or Video Generation 34311.4.8 Other Applications 34311.5 Cancer 34311.5.1 Factors 34411.5.1.1 Heredity 34511.5.1.2 Ionizing Radiation 34511.5.1.3 Chemical Substances 34511.5.1.4 Dietary Factors 34511.5.1.5 Estrogen 34611.5.1.6 Viruses 34611.5.1.7 Stress 34711.5.1.8 Age 34711.5.2 Signs and Symptoms of Cancer 34711.5.3 Types of Cancer Treatment Available 34811.5.3.1 Surgery 34811.5.3.2 Radiation Therapy 34811.5.3.3 Chemotherapy 34811.5.3.4 Immunotherapy 34811.5.3.5 Targeted Therapy 34911.5.3.6 Hormone Therapy 34911.5.3.7 Stem Cell Transplant 34911.5.3.8 Precision Medicine 34911.5.4 Types of Cancer 34911.5.4.1 Carcinoma 34911.5.4.2 Sarcoma 34911.5.4.3 Leukemia 35011.5.4.4 Lymphoma and Myeloma 35011.5.4.5 Central Nervous System (CNS) Cancers 35011.5.5 The Development of Cancer (Pathogenesis) Cancer 35011.6 Role of Deep Learning in Various Types of Cancer 35011.6.1 Skin Cancer 35111.6.1.1 Common Symptoms of Melanoma 35111.6.1.2 Types of Skin Cancer 35211.6.1.3 Prevention 35311.6.1.4 Treatment 35311.6.2 Deep Learning in Skin Cancer 35411.6.3 Pancreatic Cancer 35411.6.3.1 Symptoms of Pancreatic Cancer 35511.6.3.2 Causes or Risk Factors of Pancreatic Cancer 35511.6.3.3 Treatments of Pancreatic Cancer 35511.6.4 Deep Learning in Pancreatic Cancer 35511.6.5 Tobacco-Driven Lung Cancer 35711.6.5.1 Symptoms of Lung Cancer 35711.6.5.2 Causes or Risk Factors of Lung Cancer 35811.6.5.3 Treatments Available for Lung Cancer 35811.6.5.4 Deep Learning in Lung Cancer 35811.6.6 Breast Cancer 35911.6.6.1 Symptoms of Breast Cancer 36011.6.6.2 Causes or Risk Factors of Breast Cancer 36011.6.6.3 Treatments Available for Breast Cancer 36111.6.7 Deep Learning in Breast Cancer 36111.6.8 Prostate Cancer 36211.6.9 Deep Learning in Prostate Cancer 36211.7 Future Aspects of Deep Learning in Cancer 36311.8 Conclusion 363References 36312 Cardiovascular Disease Prediction Using Deep Neural Network for Older People 369Nagarjuna Telagam, B.Venkata Kranti and Nikhil Chandra Devarasetti12.1 Introduction 37012.2 Proposed System Model 37512.2.1 Decision Tree Algorithm 37512.2.1.1 Confusion Matrix 37612.3 Random Forest Algorithm 38112.4 Variable Importance for Random Forests 38312.5 The Proposed Method Using a Deep Learning Model 38412.5.1 Prevention of Overfitting 38612.5.2 Batch Normalization 38612.5.3 Dropout Technique 38612.6 Results and Discussions 38612.6.1 Linear Regression 38612.6.2 Decision Tree Classifier 38812.6.3 Voting Classifier 38912.6.4 Bagging Classifier 38912.6.5 Naïve Bayes 39012.6.6 Logistic Regression 39012.6.7 Extra Trees Classifier 39112.6.8 K-Nearest Neighbor [KNN] Algorithm 39112.6.9 Adaboost Classifier 39212.6.10 Light Gradient Boost Classifier 39312.6.11 Gradient Boosting Classifier 39312.6.12 Stochastic Gradient Descent Algorithm 39312.6.13 Linear Support Vector Classifier 39412.6.14 Support Vector Machines 39412.6.15 Gaussian Process Classification 39512.6.16 Random Forest Classifier 39512.7 Evaluation Metrics 39612.8 Conclusion 401References 40213 Machine Learning: The Capabilities and Efficiency of Computers in Life Sciences 407Shalini Yadav, Saurav Yadav, Shobhit Prakash Srivastava, Saurabh Kumar Gupta and Sudhanshu Mishra13.1 Introduction 40813.2 Supervised Learning 41013.2.1 Workflow of Supervised Learning 41013.2.2 Decision Tree 41013.2.3 Support Vector Machine (SVM) 41113.2.4 Naive Bayes 41313.3 Deep Learning: A New Era of Machine Learning 41413.4 Deep Learning in Artificial Intelligence (AI) 41613.5 Using ML to Enhance Preventive and Treatment Insights 41713.6 Different Additional Emergent Machine Learning Uses 41813.6.1 Education 41813.6.2 Pharmaceuticals 41913.6.3 Manufacturing 41913.7 Machine Learning 41913.7.1 Neuroscience Research Advancements 42013.7.2 Finding Patterns in Astronomical Data 42013.8 Ethical and Social Issues Raised ! ! ! 42113.8.1 Reliability and Safety 42113.8.2 Transparency and Accountability 42113.8.3 Data Privacy and Security 42113.8.4 Malicious Use of AI 42213.8.5 Effects on Healthcare Professionals 42213.9 Future of Machine Learning in Healthcare 42213.9.1 A Better Patient Journey 42213.9.2 New Ways to Deliver Care 42413.10 Challenges and Hesitations 42413.10.1 Not Overlord Assistant Intelligent 42413.10.2 Issues with Unlabeled Data 42513.11 Concluding Thoughts 425Acknowledgments 426References 426Index 431