Latest AI and machine learning research in oncology/hematology for healthcare professionals.
The 24h behaviour profile, including physical activity, sedentary time, and sleep, is disrupted following a cancer diagnosis and contributes to cancer-related outcomes. This study describes the 24h behaviour profiles of individuals with and without cancer. Seven days of accelerometer data from the UK Biobank (M±SDage = 62.3 ± 7.8y; 56.4% female) were derived by machine learning models to assess th...
Escalating thyroid nodule prevalence necessitates precise ultrasonographic diagnosis, which is constrained by operator-dependent variability. Convolutional neural network (CNN)-based artificial intelligence (AI)/machine learning (ML) frameworks can improve segmentation, malignancy prediction, and interobserver concordance, yet they often lack real-world clinical validation, interpretable architect...
To compare Digital Breast Tomosynthesis (DBT) tissue matching errors with and without artificial intelligence (AI) assistance to typical screen-detect...
Deep Learning (DL) has emerged as a powerful tool to predict genetic biomarkers directly from digitized Hematoxylin and Eosin (H&E) slides in colorect...
High-throughput proteomics has emerged as a potentially rich data source to improve capacity to forecast disease. This study explores the utility of p...
Distinguishing cell types in peripheral blood smears is critical for diagnosing blood diseases, such as leukemia subtypes. Artificial intelligence can...
Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. I...
This study aimed to develop and evaluate a machine learning pipeline using multiphase contrast-enhanced CT images and clinical data to classify renal ...
Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC). Artificial ...
BRAF status is crucial for treating pediatric low-grade gliomas (pLGG) and can be assessed non-invasively from segmented tumor regions on MRI using ma...
Multiple myeloma (MM) is characterized by abnormal plasma cell proliferation in the bone marrow, leading to symptoms like osteolytic lesions, anemia, ...
The performance and generalisability of machine learning (ML) models relies on high-quality data. Retrospective and prospective collection of high-qua...
Soft tissue sarcomas (STS) histopathological classification system has several conceptual caveats, impacting prognostication and treatment. The clinic...
Artificial intelligence (AI) foundation models such as Segment Anything Model 2 (SAM 2) offer potential for semi-automated image segmentation with min...
The global burden of cervical cancer, with a notable prevalence in regions like Tanzania, highlights the critical need for timely and accurate diagnos...
To evaluate the potential of wrist-worn wearable devices to detect and quantify Faciobrachial Dystonic Seizures (FBDS) and related events associated w...
Conventional chemotherapeutics exploit cancer’s hallmark of active cell cycling, primarily targeting mitotic cells. Consequently, the mitotic index (M...
Ovarian cancer is one of the deadliest cancers in women, with a 5-year survival rate of 17-28% in advanced stage (FIGO IIB-IV) disease and is often di...
The prognostic significance of tumor-infiltrating lymphocytes (TILs) in breast cancer has been recognized for over a decade. Although histology-based ...
Artificial Intelligence (AI) has demonstrated a high image processing capacity and improved diagnostic accuracy in dermatology. In this context, Compu...