Applied Life Sciences: Artificial Intelligence Applications for Medical CAD System Detection and Diagnosis

After cardiovascular disorders, cancer is the second most prevalent cause of mortality in many nations. Therefore, increasing the 5-year survival rate requires early diagnosis and identification. In order to diagnose diseases, screening exams are crucial, and this involves doctors interpreting a lot of medical images. Human interpretation, however, is subject to numerous constraints, such as error, exhaustion, and preoccupation. As a result, there is a risk of false positives and false negatives, which could result in inappropriate therapy. In order to overcome these constraints, a computer-aided diagnostic (CAD) system is required as a second opinion method for diagnosing unclear instances.

Image analysis techniques used by computer-aided diagnostic (CAD) systems include deep learning, machine learning, computer vision, and traditional image processing. They locate a result of interest, such as a label pointing to a diagnostic or prognosis, or a region of interest (ROI) pointing to a specific area within the provided image, using image classification or segmentation algorithms. In order to reach a final diagnosis or prognosis, this Topical Collection focuses on advanced computer-aided diagnostic (CAD) methods that employ artificial intelligence (AI) approaches in a variety of imaging modalities, including x-ray, computed tomography (CT), positron emission tomography (PET), ultrasound, magnetic resonance imaging (MRI), immunohistochemistry, and hematoxylin and eosin (H&E) whole slide images (WSIs).

This Topical Collection aims to present the state-of-the-art CAD and CADx algorithms for medical images. The topics of interest include, but are not limited to:

- Advanced Deep Learning and Machine Learning Methods for Disease Prediction

- AI-Driven Systems for Disease Risk Assessment and Early Diagnosis

- Combining Many Data Sources (Clinical, Imaging, Genetic, etc.) to Enhance Disease Prognosis

- Advanced AI Techniques That Can Be Explained to Diagnose Diseases and Suggest Treatments

- AI-Powered Analysis of Medical Images to Diseases Detection

- Utilizing Natural Language Processing to Gather and Mine Medical Data

- Challenges and Ethical Issues with AI-Driven Disease Diagnosis and Prediction

- Actual Deployment and Assessment of AI Systems in Medical Environments

- Tumor Segmentation Algorithms in Biomedical Imaging

- High-Performance CAD Algorithms for Clinical Applications

- Diagnosis and Treatment of Coronary Artery Disease

- Diagnostic Imaging and Cytopathology Testing in Tumor Detection

- Advances in Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging

- Diagnosis and Prognosis in Gastrointestinal and Liver Diseases

Keywords: artificial intelligence; machine learning; deep learning; artificial neural networks; prognosis; treatment; medicine; health care; tumor detection; biomedical imaging; diagnosis and prognosis; diseases detection and diagnosis