BACKGROUND: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intellige...
This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, ...
PURPOSE: A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses.
OBJECTIVE: This study investigated whether artificial intelligence (AI) models combining voice signals, demographics, and structured medical records can detect glottic neoplasm from benign voice disorders.
Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the current NCCN Guidelines. Thus, the classification will be associated with th...
PURPOSE: To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy o...
Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
Jun 4, 2024
BACKGROUND: Neural tumors are difficult to distinguish based solely on cellularity and often require immunohistochemical staining to aid in identifying the cell lineage. This article investigates the potential of a Convolutional Neural Network for th...
BACKGROUND: Machine learning (ML) offers an opportunity in contact dermatitis (CD) research, where with full clinical picture, may support diagnosis and patch test accuracy.
OBJECTIVE: In this study, our aim was to develop and validate the effectiveness of diverse radiomic models for distinguishing between gnathic fibrous dysplasia (FD) and ossifying fibroma (OF) before surgery.