AI Medical Compendium Topic

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Kidney

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Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections.

Abdominal radiology (New York)
PURPOSE: Abdominal ultrasound screening requires the capture of multiple standardized plane views as per clinical guidelines. Currently, the extent of adherence to such guidelines is dependent entirely on the skills of the sonographer. The use of neu...

A new, deep learning-based method for the analysis of autopsy kidney samples used to study sex differences in glomerular density and size in a forensic population.

International journal of legal medicine
Artificial intelligence (AI) is increasingly used in forensic anthropology and genetics to identify the victim and the cause of death. The large autopsy samples from persons with traumatic causes of death but without comorbidities also offer possibil...

A machine learning approach for quantifying age-related histological changes in the mouse kidney.

GeroScience
The ability to quantify aging-related changes in histological samples is important, as it allows for evaluation of interventions intended to effect health span. We used a machine learning architecture that can be trained to detect and quantify these ...

A 5G-based telerobotic ultrasound system provides qualified abdominal ultrasound services for patients on a rural island: a prospective and comparative study of 401 patients.

Abdominal radiology (New York)
PURPOSE: To explore the feasibility of a 5G-based telerobotic ultrasound (US) system for providing qualified abdominal US services on a rural island.

Multimodal deep learning for personalized renal cell carcinoma prognosis: Integrating CT imaging and clinical data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Renal cell carcinoma represents a significant global health challenge with a low survival rate. The aim of this research was to devise a comprehensive deep-learning model capable of predicting survival probabilities in patie...

Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data.

Clinical imaging
PURPOSE: This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images ...

Comparison of Percutaneous Renal Access Between Robot-Assisted Fluoroscopy Guidance Using the Bi-Plane Method and Ultrasound Guidance: A Multicenter Randomized Control Benchtop Study.

Journal of endourology
To evaluate the efficacy of supine percutaneous renal access by robot-assisted (RA) fluoroscopy and ultrasound (US) guidance in terms of procedural outcomes and surgeon workload. We conducted a multicenter, randomized, controlled benchtop study inv...

Dynamic parametric MRI and deep learning: Unveiling renal pathophysiology through accurate kidney size quantification.

NMR in biomedicine
Renal pathologies often manifest as alterations in kidney size, providing a valuable avenue for employing dynamic parametric MRI as a means to derive kidney size measurements for the diagnosis, treatment, and monitoring of renal disease. Furthermore,...

FastCellpose: A Fast and Accurate Deep-Learning Framework for Segmentation of All Glomeruli in Mouse Whole-Kidney Microscopic Optical Images.

Cells
Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical ima...