AIMC Topic: Retrospective Studies

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Performance of an artificial intelligence-based diagnostic support tool for early gastric cancers: Retrospective study.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: Endoscopists' abilities to diagnose early gastric cancers (EGCs) vary, especially between specialists and nonspecialists. We developed an artificial intelligence (AI)-based diagnostic support tool "Tango" to differentiate EGCs and compare...

The learning curve of imageless robot-assisted total knee arthroplasty with standardised laxity testing requires the completion of nine cases, but does not reach time neutrality compared to conventional surgery.

International orthopaedics
PURPOSE: The assistance of robot technology is introduced into the operating theatre to improve the precision of a total knee arthroplasty. However, as with all new technology, new technology requires a learning curve to reach adequate proficiency. T...

Preliminary Surgical Outcomes After Single Incision Robotic Cystectomy (SIRC).

Urology
OBJECTIVE: To report the preliminary surgical outcomes for single incision robotic cystectomy (SIRC). Robotic cystectomy is associated with low utilization rates of orthotopic neobladders due to challenges related to intracorporeal sowing and configu...

Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and/or deep learning-based radiomics features.

Radiation oncology (London, England)
BACKGROUND: This study was designed to establish radiation pneumonitis (RP) prediction models using dosiomics and/or deep learning-based radiomics (DLR) features based on 3D dose distribution.

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography.

Scientific reports
Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the perfor...

Identification of hospitalized mortality of patients with COVID-19 by machine learning models based on blood inflammatory cytokines.

Frontiers in public health
Coronavirus disease 2019 (COVID-19) spread worldwide and presented a significant threat to people's health. Inappropriate disease assessment and treatment strategies bring a heavy burden on healthcare systems. Our study aimed to construct predictive ...

Long-term oncologic outcomes of robot-assisted radical cystectomy: update series from a high-volume robotic center beyond 10 years of follow-up.

Journal of robotic surgery
Long-term oncologic data on patients undergoing robot-assisted radical cystectomy (RARC) for non-metastatic bladder cancer (BCa) are limited. The purpose of this study is to describe long-term oncologic outcomes of patients receiving robotic radical ...

MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.

European radiology
OBJECTIVES: The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI.

Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist.

European radiology
OBJECTIVES: To assess how an artificial intelligence (AI) algorithm performs against five experienced musculoskeletal radiologists in diagnosing scaphoid fractures and whether it aids their diagnosis on conventional multi-view radiographs.

Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer.

Radiology
Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted...