AIMC Topic: Humans

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Artificial neural network prediction of postoperative complications in papillary thyroid microcarcinoma based on preoperative ultrasonographic features.

Journal of clinical ultrasound : JCU
OBJECTIVE: To predict post-thyroidectomy complications in papillary thyroid microcarcinoma (PTMC) patients using a deep learning model based on preoperative ultrasonographic features. This study addresses the global rise in PTMC incidence and the cha...

Assessment of chemical contamination by cancer drugs during use of the RIVA compounding robot: A pilot study.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
IntroductionMany hospitals are now investing in robotic compounding system for the preparation of cytotoxic agents. The objective of the present study was to describe contamination by cytotoxics inside and outside the RIVA robot (ARxIUM, Winnipeg, Ca...

Informatics-driven solutions for optimal care delivery in liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
Clinical informatics, which combines health information technology and clinical expertise, aims to improve health care delivery and outcomes. For candidates and recipients of liver transplants, the complexities of their management are vast. Care ofte...

Utility of large language models for creating clinical assessment items.

Medical teacher
PURPOSE: To compare student performance, examiner perceptions and cost of GPT-assisted (generative pretrained transformer-assisted) clinical and professional skills assessment (CPSAs) items against items created using standard methods.

Improved Automated Quality Control of Skeletal Wrist Radiographs Using Deep Multitask Learning.

Journal of imaging informatics in medicine
Radiographic quality control is an integral component of the radiology workflow. In this study, we developed a convolutional neural network model tailored for automated quality control, specifically designed to detect and classify key attributes of w...

A 3D Convolutional Neural Network Based on Non-enhanced Brain CT to Identify Patients with Brain Metastases.

Journal of imaging informatics in medicine
Dedicated brain imaging for cancer patients is seldom recommended in the absence of symptoms. There is increasing availability of non-enhanced CT (NE-CT) of the brain, mainly owing to a wider utilization of Positron Emission Tomography-CT (PET-CT) in...

The Fine-Tuned Large Language Model for Extracting the Progressive Bone Metastasis from Unstructured Radiology Reports.

Journal of imaging informatics in medicine
Early detection of patients with impending bone metastasis is crucial for prognosis improvement. This study aimed to investigate the feasibility of a fine-tuned, locally run large language model (LLM) in extracting patients with bone metastasis in un...

Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning.

Academic radiology
RATIONALE AND OBJECTIVES: The structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with compute...

Asynchronous iterative Q-learning based tracking control for nonlinear discrete-time multi-agent systems.

Neural networks : the official journal of the International Neural Network Society
This paper addresses the tracking control problem of nonlinear discrete-time multi-agent systems (MASs). First, a local neighborhood error system (LNES) is constructed. Then, a novel tracking algorithm based on asynchronous iterative Q-learning (AIQL...