Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys.

Efficient algorithms are needed to segment vasculature in new three-dimensional (3D) medical imaging datasets at scale for a wide range of research and clinical applications. Manual segmentation of vessels in images is time-consuming and expensive. Computational approaches are more scalable but have limitations in accuracy.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily.

Experimental Analysis of Robotic Cortical Bone Specimen Drilling Performance: Effect of Cryogen.

In orthopedic surgery, precise bone screw insertion is crucial for stabilizing fractures, necessitating a preliminary cortical bone drilling procedure. However, this process can induce temperatures exceeding 70 °C due to the low thermal conductivity of cortical bone, potentially leading to thermal osteonecrosis. Furthermore, significant cutting forces and torque pose risks of tool breakage and bone damage, underlining the need for high precision and optimal processing parameters.

Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury.

The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases.

Correction: Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study.

[This corrects the article DOI: 10.2196/24651.].

Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study.

Background: Appropriate empirical treatment for candidemia is associated with reduced mortality; however, the timely diagnosis of candidemia in patients with sepsis remains poor.

Objective: We aimed to use machine learning algorithms to develop and validate a candidemia prediction model for patients with cancer.

Methods: We conducted a single-center retrospective study using the cancer registry of a tertiary academic hospital.

A Machine Learning-Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study.

Background: Delirium frequently occurs among patients admitted to the intensive care unit (ICU). There is limited evidence to support interventions to treat or resolve delirium in patients who have already developed delirium. Therefore, the early recognition and prevention of delirium are important in the management of critically ill patients.

Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea.

Background: Out-of-hospital cardiac arrest (OHCA) is a serious public health issue, and predicting the prognosis of OHCA patients can assist clinicians in making decisions about the treatment of patients, use of hospital resources, or termination of resuscitation.

Objective: This study aimed to develop a time-adaptive conditional prediction model (TACOM) to predict clinical outcomes every minute.

Methods: We performed a retrospective observational study using data from the Korea OHCA Registry in South Korea.