The ULS23 challenge: A baseline model and benchmark dataset for 3D universal lesion segmentation in computed tomography.

Size measurements of tumor manifestations on follow-up CT examinations are crucial for evaluating treatment outcomes in cancer patients. Efficient lesion segmentation can speed up these radiological workflows. While numerous benchmarks and challenges address lesion segmentation in specific organs like the liver, kidneys, and lungs, the larger variety of lesion types encountered in clinical practice demands a more universal approach.

Artificial intelligence for the detection of airway nodules in chest CT scans.

Objectives: Incidental airway tumors are rare and can easily be overlooked on chest CT, especially at an early stage. Therefore, we developed and assessed a deep learning-based artificial intelligence (AI) system for detecting and localizing airway nodules.

Materials And Methods: At a single academic hospital, we retrospectively analyzed cancer diagnoses and radiology reports from patients who received a chest or chest-abdomen CT scan between 2004 and 2020 to find cases presenting as airway nodules.

Rare Case of Refractory Shock Secondary to Large B Cell Lymphoma.

A previously healthy 69-year-old female admitted to the hospital with refractory hypotension fevers and diarrhea. She had two prior hospitalizations with similar presentations and no clear etiology could be identified. During her current hospitalization, she was admitted to the intensive care unit (ICU) due to refractory shock.

Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection.

Artificial Intelligence (AI) models may fail or suffer from reduced performance when applied to unseen data that differs from the training data distribution, referred to as dataset shift. Automatic detection of out-of-distribution (OOD) data contributes to safe and reliable clinical implementation of AI models. In this study, we propose a recognized OOD detection method that utilizes the Mahalanobis distance (MD) and compare its performance to widely known classical methods.

Evaluation of participation in a former rehabilitation population: a comparison between USER-Participation Restriction and Satisfaction subscales and PROMIS Ability to Participate and PROMIS Satisfaction with participation 8-item short forms in a cross-sectional multicentre study.

Background: Several Patient Reported Outcome Measurements (PROMs) can be used to quantify participation in rehabilitation patients, yet there is limited comparative research on their content and psychometric properties to make an informed decision between them.

Objective: To compare the content and several psychometric properties of the Restriction and Satisfaction subscales of the Utrecht Scale for Evaluation of Rehabilitation - Participation (USER-P) with the Patient-Reported Outcomes Measurement Information System Ability to Participate in Social Roles and Activities (PROMIS-APS) and Satisfaction with Social Roles and Activities (PROMIS-SPS) v2.0 8-item short forms.