Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Artificial Intelligence In The Management Of Leukemia, Stephanie Koo, Austin P. Runde, Melvin Speisman

School of Medicine

BACKGROUND: Recently, given the demonstrated ability of AI to accurately characterize complex pathologies, AI has been proposed to be of use in the diagnosis, treatment, and monitoring of leukemias given their genetic complexity and subtype heterogeneity, array of treatments, and need for relapse detection. AI has several potential applications in the management of leukemia. First, it can be used to detect leukemia; using AI to detect nuances in lab values can ensure these deadly cancers are never missed. Second, AI can be used to risk-stratify patients and personalize treatments; leukemias are among the most genetically complex cancers with well-characterized risk …

A Review Of Racial Differences And Disparities In Ecg, Jianwei Zheng, Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei, Omolola Ogunyemi

Mathematics, Physics, and Computer Science Faculty Articles and Research

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major …

Visual Artificial Intelligence In Healthcare: A Revolution In Making, Sanjay S. Rao, Punnya. V. Angadi

Indian Journal of Health Sciences and Biomedical Research KLEU

Visual artificial intelligence (AI) is a branch of computer science that teaches robots to understand images and visual information similarly to how humans do. According to the algorithm's development, visual AI allows robots to do more than just perceive; it also allows them to understand and sense the meaning behind images. Because of the enormous progress made in this area, computers are now able to recognize and interpret images more accurately than humans. From managing medical data to enhancing care delivery through AI-assisted diagnosis, visual AI has a broad impact on healthcare. By building devices and tools that can learn, …

Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa

Journal of Dentistry Indonesia

Image representation via machine learning is an approach to quantitatively represent histopathological images of head and neck tumors for future applications of artificial intelligence-assisted pathological diagnosis systems. Objective: This study compares image representations produced by a pre-trained convolutional neural network (VGG16) to those produced by a vision transformer (ViT-L/14) in terms of the classification performance of head and neck tumors. Methods: W hole-slide images of five oral t umor categories (n = 319 cases) were analyzed. Image patches were created from manually annotated regions at 4096, 2048, and 1024 pixels and rescaled to 256 pixels. Image representations were …

Patient And Provider Experience With Artificial Intelligence Screening Technology For Diabetic Retinopathy In A Rural Primary Care Setting, Brian M. Nolan, Emma R. Daybranch, Kerri Barton, Neil Korsen

Journal of Maine Medical Center

Introduction: The development of autonomous artificial intelligence for interpreting diabetic retinopathy (DR) images has allowed for point-of-care testing in the primary care setting. This study describes patient and provider experiences and perceptions of the artificial intelligence DR screening technology called EyeArt by EyeNuk during implementation of the tool at Western Maine Primary Care in Norway, Maine.

Methods: This non-randomized, single-center, prospective observational study surveyed 102 patients and 13 primary care providers on their experience of the new screening intervention.

Results: All surveyed providers agreed that the new screening tool would improve access and annual screening rates. Some providers also identified …