AIMC Topic: Prospective Studies

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Systematic Review and Meta-Analysis of Pediatric Robot-Assisted Laparoscopic Pyeloplasty.

Journal of endourology
To perform a systematic review (SR) and meta-analysis (MA) of outcomes of robot-assisted laparoscopic pyeloplasty (RALP) for ureteropelvic junction (UPJ) obstruction in children. A SR of the English-language literature on surgical techniques and pe...

Robot-assisted Versus Conventional Minimally Invasive Esophagectomy for Resectable Esophageal Squamous Cell Carcinoma: Early Results of a Multicenter Randomized Controlled Trial: the RAMIE Trial.

Annals of surgery
OBJECTIVE: To compare perioperative and long-term outcomes of robot-assisted minimally invasive esophagectomy (RAMIE) and conventional minimally invasive esophagectomy (MIE) in the treatment for patients with esophageal squamous cell carcinoma (ESCC)...

Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study.

The Lancet. Digital health
BACKGROUND: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model that aut...

Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVES: To develop and validate an artificial intelligence system, the Prenatal ultrasound diagnosis Artificial Intelligence Conduct System (PAICS), to detect different patterns of fetal intracranial abnormality in standard sonographic reference ...

The efficacy of robot-assisted training for patients with upper limb amputations who use myoelectric prostheses: a randomized controlled pilot study.

International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation
The aim of this pilot study was to investigate whether a movement therapy robot can improve skills in using a myoelectric prosthesis by patients with upper limb amputations. This prospective randomized, controlled study included a total of eleven pat...

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis.

The Pediatric infectious disease journal
BACKGROUND: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk f...

Simple Linear Cancer Risk Prediction Models With Novel Features Outperform Complex Approaches.

JCO clinical cancer informatics
PURPOSE: The ability to accurately predict an individual's risk for cancer is critical to the implementation of precision prevention measures. Current cancer risk predictions are frequently made with simple models that use a few proven risk factors, ...

Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.

Neuro-oncology
BACKGROUND: Longitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric...

Validation of a Machine Learning Model for Early Shock Detection.

Military medicine
OBJECTIVES: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity...

Artificial intelligence guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records.

Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko
BACKGROUND: Rational use of internal resources of hospitals including bed fund turnover is important objective in high-tech medicine. Machine learning technologies can improve neurosurgical care and contribute to patient-oriented approach.