Artificial intelligence in image-based screening, diagnostics, and clinical care of cardiopulmonary diseases / edited by Sameer Antani, Sivaramakrishnan Rajaraman.

Format:

Book

Contributor:

Antani, Sameer, editor.

Rajaraman, Sivaramakrishnan, editor.

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Physical Description:

1 online resource (246 pages)

Place of Publication:

Basel MDPI - Multidisciplinary Digital Publishing Institute 2023

Basel, Switzerland : MDPI - Multidisciplinary Digital Publishing Institute, [2023]

Language Note:

English

Summary:

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, "Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases", we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI.

Contents:

About the Editors vii

Sivaramakrishnan Rajaraman and Sameer Antani Editorial on Special Issue "Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases" Reprinted from: Diagnostics 2022, 12, 2615, doi:10.3390/diagnostics12112615 1

Noemi Gozzi, Edoardo Giacomello, Martina Sollini, Margarita Kirienko, Angela Ammirabile, Pierluca Lanzi, Daniele Loiacono, et al. Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs Reprinted from: Diagnostics 2022, 12, 2084, doi:10.3390/diagnostics12092084 9

Abdulaziz Fahad AlOthman, Abdul Rahaman Wahab Sait and Thamer Abdullah Alhussain Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique Reprinted from: Diagnostics 2022, 12, 2073, doi:10.3390/diagnostics12092073 29

Guan-Hua Huang, Qi-Jia Fu, Ming-Zhang Gu, Nan-Han Lu, Kuo-Ying Liu and Tai-Been Chen Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images Reprinted from: Diagnostics 2022, 12, 1457, doi:10.3390/diagnostics12061457 49

Sivaramakrishnan Rajaraman, Peng Guo, Zhiyun Xue and Sameer K. Antani A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays Reprinted from: Diagnostics 2022, 12, 1442, doi:10.3390/diagnostics12061442 67

Hao-Jen Wang, Li-Wei Chen, Hsin-Ying Lee, Yu-Jung Chung, Yan-Ting Lin, Yi-Chieh Lee, Yi-Chang Chen, et al. Automated 3D Segmentation of the Aorta and Pulmonary Artery on Non-Contrast-Enhanced Chest Computed Tomography Images in Lung Cancer Patients Reprinted from: Diagnostics 2022, 12, 967, doi:10.3390/diagnostics12040967 . 83

Jöran Rixen, Benedikt Eliasson, Benjamin Hentze, Thomas Muders, Christian Putensen, Steffen Leonhardt and Chuong Ngo A Rotational Invariant Neural Network for Electrical Impedance Tomography Imaging without Reference Voltage: RF-REIM-NET Reprinted from: Diagnostics 2022, 12, 777, doi:10.3390/diagnostics12040777 . 99

Manohar Karki, Karthik Kantipudi, Feng Yang, Hang Yu, Yi Xiang J. Wang, Ziv Yaniv and Stefan Jaeger Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays Reprinted from: Diagnostics 2022, 12, 188, doi:10.3390/diagnostics12010188 . 115

Philippe Germain, Armine Vardazaryan, Nicolas Padoy, Aissam Labani, Catherine Roy, Thomas Hellmut Schindler and Soraya El Ghannudi Deep Learning Supplants Visual Analysis by Experienced Operators for the Diagnosis of Cardiac Amyloidosis by Cine-CMR Reprinted from: Diagnostics 2022, 12, 69, doi:10.3390/diagnostics12010069 . 139

Muhammad Attique Khan, Venkatesan Rajinikanth, Suresh Chandra Satapathy, David Taniar, Jnyana Ranjan Mohanty, Usman Tariq and Robertas Damaševiˇcius VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images Reprinted from: Diagnostics 2021, 11, 2208, doi:10.3390/diagnostics11122208 153

Jasjit S. Suri, Sushant Agarwal, Pranav Elavarthi, Rajesh Pathak, Vedmanvitha Ketireddy, Marta Columbu, Luca Saba, et al. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography Reprinted from: Diagnostics 2021, 11, 2025, doi:10.3390/diagnostics11112025 171

Julia A. Mueller, Katharina Martini, Matthias Eberhard, Mathias A. Mueller, Alessandra A. De Silvestro, Philipp Breiding and Thomas Frauenfelder Diagnostic Performance of Dual-Energy Subtraction Radiography for the Detection of Pulmonary Emphysema: An Intra-Individual Comparison Reprinted from: Diagnostics 2021, 11, 1849, doi:10.3390/diagnostics11101849 207

Dana Li, Lea Marie Pehrson, Carsten Ammitzbøl Lauridsen, Lea Tøttrup, Marco Fraccaro, Desmond Elliott, Hubert Dariusz Zaj ˛ac, et al. The Added Effect of Artificial Intelligence on Physicians' Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review Reprinted from: Diagnostics 2021, 11, 2206, doi:10.3390/diagnostics11122206 219.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-0365-6435-7