Out-of-distribution generalization for segmentation of lymph node metastasis in breast cancer.

Pathology provides the definitive diagnosis, and Artificial Intelligence (AI) tools are poised to improve accuracy, inter-rater agreement, and turn-around time (TAT) of pathologists, leading to improved quality of care. A high value clinical application is the grading of Lymph Node Metastasis (LNM) which is used for breast cancer staging and guides treatment decisions. A challenge of implementing AI tools widely for LNM classification is domain shift, where Out-of-Distribution (OOD) data has a different distribution than the In-Distribution (ID) data used to train the model, resulting in a drop in performance in OOD data.

Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

Purpose: This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia patients. Addressing the challenge of sparse datasets in rare disease diagnostics, the study aims to enhance AI model robustness and accuracy through the integration of domain knowledge into the synthetic image generation process.

Methods: The study employed two ultrasound datasets: a base dataset (Db) of knee recess distension images from non-hemophiliac patients and a target dataset (Dt) of hemarthrosis images from hemophiliac patients.

Towards a histological diagnosis of childhood small vessel CNS vasculitis.

Background: Primary small vessel CNS vasculitis (sv-cPACNS) is a challenging inflammatory brain disease in children. Brain biopsy is mandatory to confirm the diagnosis. This study aims to develop and validate a histological scoring tool for diagnosing small vessel CNS vasculitis.

Utilizing artificial intelligence for the detection of hemarthrosis in hemophilia using point-of-care ultrasonography.

Background: Recurrent hemarthrosis and resultant hemophilic arthropathy are significant causes of morbidity in persons with hemophilia, despite the marked evolution of hemophilia care. Prevention, timely diagnosis, and treatment of bleeding episodes are key. However, a physical examination or a patient's assessment of musculoskeletal pain may not accurately identify a joint bleed.

Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease.