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Biopsy

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Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy.

BMC urology
BACKGROUND: Machine learning has many attractive theoretic properties, specifically, the ability to handle non predefined relations. Additionally, studies have validated the clinical utility of mpMRI for the detection and localization of CSPCa (Gleas...

Validation of a machine learning approach using FIB-4 and APRI scores assessed by the metavir scoring system: A cohort study.

Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology
BACKGROUND AND STUDY AIM: The study aim was to improve and validate the accuracy of the fibrosis-4 (FIB-4) and aspartate aminotransferase-to-platelet ratio index (APRI) scores for use in a potential machine-learning (ML) method that accurately predic...

Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies.

Scientific reports
To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic ...

Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images.

JAMA network open
IMPORTANCE: Interstitial fibrosis and tubular atrophy (IFTA) is a strong indicator of decline in kidney function and is measured using histopathological assessment of kidney biopsy core. At present, a noninvasive test to assess IFTA is not available.

Improving DCIS diagnosis and predictive outcome by applying artificial intelligence.

Biochimica et biophysica acta. Reviews on cancer
Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predic...

Independent real-world application of a clinical-grade automated prostate cancer detection system.

The Journal of pathology
Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists...

Time-aware deep neural networks for needle tip localization in 2D ultrasound.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate placement of the needle is critical in interventions like biopsies and regional anesthesia, during which incorrect needle insertion can lead to procedure failure and complications. Therefore, ultrasound guidance is widely used to im...

Deep learning-based molecular morphometrics for kidney biopsies.

JCI insight
Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific...

Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides.

Scientific reports
Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC...