AIMC Topic: Retrospective Studies

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Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study.

The Lancet. Oncology
BACKGROUND: Accurate lymph node staging is important for the diagnosis and treatment of patients with bladder cancer. We aimed to develop a lymph node metastases diagnostic model (LNMDM) on whole slide images and to assess the clinical effect of an a...

Risk factors and prediction model for inadvertent intraoperative hypothermia in patients undergoing robotic surgery: a retrospective analysis.

Scientific reports
This study explored the risk factors and established a prediction model for intraoperative hypothermia (IOH) in patients undergoing robotic surgery. We conducted a retrospective survey of patients undergoing elective robotic surgery at the China-Japa...

Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: a retrospective study.

Scientific reports
We explored a new artificial intelligence-assisted method to assist junior ultrasonographers in improving the diagnostic performance of uterine fibroids and further compared it with senior ultrasonographers to confirm the effectiveness and feasibilit...

Explainable deep learning-based clinical decision support engine for MRI-based automated diagnosis of temporomandibular joint anterior disk displacement.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: MRI is considered the gold standard for diagnosing anterior disc displacement (ADD), the most common temporomandibular joint (TMJ) disorder. However, even highly trained clinicians find it difficult to integrate the dynamic ...

Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Radiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task d...

Evaluation of the clinical performance of an AI-based application for the automated analysis of chest X-rays.

Scientific reports
The AI-Rad Companion Chest X-ray (AI-Rad, Siemens Healthineers) is an artificial-intelligence based application for the analysis of chest X-rays. The purpose of the present study is to evaluate the performance of the AI-Rad. In total, 499 radiographs...

Comparison of Deep-Learning Image Reconstruction With Hybrid Iterative Reconstruction for Evaluating Lung Nodules With High-Resolution Computed Tomography.

Journal of computer assisted tomography
OBJECTIVE: This study aimed to investigate the impact of deep-learning reconstruction (DLR) on the detailed evaluation of solitary lung nodule using high-resolution computed tomography (HRCT) compared with hybrid iterative reconstruction (hybrid IR).

Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND AND AIMS: Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learning-based system named Intelligent Detection Endoscopic Assistant-Helicobacter pylori (IDEA-HP) to ass...

Deep learning-based prediction of rib fracture presence in frontal radiographs of children under two years of age: a proof-of-concept study.

The British journal of radiology
OBJECTIVE: In this proof-of-concept study, we aimed to develop deep-learning-based classifiers to identify rib fractures on frontal chest radiographs in children under 2 years of age.

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

Frontiers in cellular and infection microbiology
BACKGROUND: There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVI...