AIMC Topic: Male

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Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

Abdominal radiology (New York)
PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T-weighted MR imaging (TWI) for gastric cancer (GC).

A deep learning-based pipeline for developing multi-rib shape generative model with populational percentiles or anthropometrics as predictors.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Rib cross-sectional shapes (characterized by the outer contour and cortical bone thickness) affect the rib mechanical response under impact loading, thereby influence the rib injury pattern and risk. A statistical description of the rib shapes or the...

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Bone
Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bla...

Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome Using Machine Learning Based Predictions.

Urology
OBJECTIVE: To improve diagnosis of interstitial cystitis (IC)/bladder pain syndrome(IC) we hereby developed an improved IC risk classification using machine learning algorithms.

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches.

Epilepsy & behavior : E&B
PURPOSE: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach...

Implementation of artificial intelligence-based computer vision model in laparoscopic appendectomy: validation, reliability, and clinical correlation.

Surgical endoscopy
BACKGROUND: Application of artificial intelligence (AI) in general surgery is evolving. Real-world implementation of an AI-based computer-vision model in laparoscopic appendectomy (LA) is presented. We aimed to evaluate (1) its accuracy in complexity...

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.

World journal of surgical oncology
BACKGROUND: The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that ...

Care-receivers with physical disabilities' perceptions on having humanoid assistive robots as assistants: a qualitative study.

BMC health services research
BACKGROUND: People with physical disabilities due to disease or injury face barriers to their daily activities and participation in society. Many depend on formal or informal caregivers for assistance to live independently. However, future healthcare...

Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method.

BMC pulmonary medicine
BACKGROUND: Pneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces ch...

Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning.

BMC medical informatics and decision making
OBJECTIVE: This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs).