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Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study.

Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolimus concentration. Data from 443 patients undergoing liver transplantation between 2017 and 2020 at an academic hospital in South Korea were collected ...

Predictors of residual tricuspid regurgitation after interventional therapy: an automated deep-learning CT analysis.

Computed tomography (CT) is used as a valuable tool for device selection for interventional therapy in tricuspid regurgitation (TR). We aimed to evaluate predictors of TR reduction using CT and automated deep learning algorithms. Patients with severe to torrential TR and CTs prior to either percutaneous annuloplasty (PA) or tricuspid transcatheter edge-to-edge repair (T-TEER) were enrolled. CTs we...

Deep Learning and fMRI-Based Pipeline for Optimization of Deep Brain Stimulation During Parkinson's Disease Treatment: Toward Rapid Semi-Automated Stimulation Optimization.

OBJECTIVE: Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the pos...

Robotic MR-guided high dose rate brachytherapy needle implantation in the prostate (ROBiNSon)-a proof-of-concept study.

A robotic needle implant device for MR-guided high-dose-rate (HDR) prostate brachytherapy was developed. This study aimed to assess the feasibility an...

Through a computer monitor darkly: artificial intelligence in absorption, distribution, metabolism and excretion science.

Artificial Intelligence (AI) is poised or has already begun to influence absorption, distribution, metabolism and excretion (ADME) science. It is not ...

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.

The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Developme...

Deep Learning-Based Prediction of Post-treatment Survival in Hepatocellular Carcinoma Patients Using Pre-treatment CT Images and Clinical Data.

The objective of this study was to develop and evaluate a model for predicting post-treatment survival in hepatocellular carcinoma (HCC) patients usin...

Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.

Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk factors for PONV is crucial because it is associat...

Feasibility of Adjunct Therapy with a Robotic Hand Orthosis after Botulinum Toxin Injections in Persons with Spasticity: A Pilot Study.

Upper-limb spasticity, frequent after central nervous system lesions, is typically treated with botulinum neurotoxin type A (BoNT-A) injections to red...

Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data.

This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, a...

Estimating highest capacity propulsion performance using backward-directed force during walking evaluation for individuals with acquired brain injury.

There are over 5.3 million Americans who face acquired brain injury (ABI)-related disability as well as almost 800,000 who suffer from stroke each yea...

MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions.

Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoske...

Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds.

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical ...

EfficientQ: An efficient and accurate post-training neural network quantization method for medical image segmentation.

Model quantization is a promising technique that can simultaneously compress and accelerate a deep neural network by limiting its computation bit-widt...

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep...

Multimodal distribution and its impact on the accurate assessment of spermatozoa morphological data: Lessons from machine learning.

Objective assessment of sperm morphology is an essential component for assessing ejaculate quality. Due to economic limitations, investigators often d...

Finite element models with automatic computed tomography bone segmentation for failure load computation.

Bone segmentation is an important step to perform biomechanical failure load simulations on in-vivo CT data of patients with bone metastasis, as it is...

AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients ...

Direct Comparisons of Upper-Limb Motor Learning Performance Among Three Types of Haptic Guidance With Non-Assisted Condition in Spiral Drawing Task.

In robot-assisted rehabilitation, it is unclear which type of haptic guidance is effective for regaining motor function because of the lack of direct ...

Predictive Modeling of Long-Term Prognosis After Resection in Typical Pulmonary Carcinoid: A Machine Learning Perspective.

Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes ...