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Unsupervised Machine Learning to Identify Risk Factors of Pyeloplasty Failure in Ureteropelvic Junction Obstruction.
Journal of endourology
Sep 17, 2024
In adult patients with ureteropelvic junction obstruction (UPJO), little data exist on predicting pyeloplasty outcome, and there is no unified definition of pyeloplasty success. As such, defining pyeloplasty success retrospectively is particularly v...
Sensory Stimulation and Robot-Assisted Arm Training After Stroke: A Randomized Controlled Trial.
Journal of neurologic physical therapy : JNPT
Sep 17, 2024
BACKGROUND AND PURPOSE: Functional recovery after stroke is often limited, despite various treatment methods such as robot-assisted therapy. Repetitive sensory stimulation (RSS) might be a promising add-on therapy that is thought to directly drive pl...
Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes.
BMC gastroenterology
Sep 17, 2024
BACKGROUND: Colorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have show...
Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark.
Nature communications
Sep 17, 2024
The detection and tracking of metastatic cancer over the lifetime of a patient remains a major challenge in clinical trials and real-world care. Advances in deep learning combined with massive datasets may enable the development of tools that can add...
Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.
Journal of medical Internet research
Sep 17, 2024
BACKGROUND: Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been...
Predicting stroke volume variation using central venous pressure waveform: a deep learning approach.
Physiological measurement
Sep 17, 2024
. This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.. Long short-term memory (LSTM) and the feed-forward neural network were sequenced to ...
Deep Learning Algorithm‑Based MRI Radiomics and Pathomics for Predicting Microsatellite Instability Status in Rectal Cancer: A Multicenter Study.
Academic radiology
Sep 16, 2024
RATIONALE AND OBJECTIVES: To develop and validate multimodal deep-learning models based on clinical variables, multiparametric MRI (mp-MRI) and hematoxylin and eosin (HE) stained pathology slides for predicting microsatellite instability (MSI) status...
Intratumoral and Peritumoral Radiomics for Predicting the Prognosis of High-grade Serous Ovarian Cancer Patients Receiving Platinum-Based Chemotherapy.
Academic radiology
Sep 16, 2024
RATIONALE AND OBJECTIVES: This study aimed to develop a deep learning (DL) prognostic model to evaluate the significance of intra- and peritumoral radiomics in predicting outcomes for high-grade serous ovarian cancer (HGSOC) patients receiving platin...
Advancements in opportunistic intracranial aneurysm screening: The impact of a deep learning algorithm on radiologists' analysis of T2-weighted cranial MRI.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Sep 16, 2024
(1) Background: Unruptured Intracranial Aneurysms (UIAs) are common blood vessel malformations, occurring in up to 3 % of healthy adults. Magnetic Resonance Angiography (MRA) is frequently used for the screening of UIAs due to its high resolution in ...
A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms.
Annals of medicine
Sep 16, 2024
OBJECTIVE: To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB).
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