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Applications of artificial intelligence in urologic oncology.

Investigative and clinical urology
PURPOSE: With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urolog...

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Radiology
Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models...

Integrating Machine Learning and Traditional Survival Analysis to Identify Key Predictors of Foveal Involvement in Geographic Atrophy.

Investigative ophthalmology & visual science
PURPOSE: The purpose of this study was to investigate the incidence of foveal involvement in geographic atrophy (GA) secondary to age-related macular degeneration (AMD), using machine learning to assess the importance of risk factors.

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing.

Journal of cellular and molecular medicine
We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcript...

Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

Investigative ophthalmology & visual science
PURPOSE: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This study aims to utilize a deep-learning (DL)-based automated segmentation model to segment orbital muscl...

Does the Internet Provide Quality Information on Metoidioplasty? Using the Modified Ensuring Quality Information for Patients Tool to Evaluate Artificial Intelligence-Generated and Online Information on Metoidioplasty.

Annals of plastic surgery
BACKGROUND: Public interest in gender affirmation surgery has surged over the last decade. This spike in interest, combined with extensive free online medical knowledge, has led many to the Internet for more information on this complicated procedure....

A Machine Learning Model to Predict the Histology of Retroperitoneal Lymph Node Dissection Specimens.

Anticancer research
BACKGROUND/AIM: While post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) benefits patients with teratoma or viable germ cell tumors (GCT), it becomes overtreatment if necrosis is detected in PC-RPLND specimens. Serum microRNA-371a-3p ...

AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study.

The Lancet. Digital health
BACKGROUND: Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances ...

Evaluation of a machine-learning model based on laboratory parameters for the prediction of acute leukaemia subtypes: a multicentre model development and validation study in France.

The Lancet. Digital health
BACKGROUND: Acute leukaemias are life-threatening haematological cancers characterised by the infiltration of transformed immature haematopoietic cells in the blood and bone marrow. Prompt and accurate diagnosis of the three main acute leukaemia subt...

Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system.

The Lancet. Digital health
BACKGROUND: In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depending on the patient population studied. We aimed to analyse severe COVID-19 outcomes and to investig...