Toward Versatile Small Object Detection with Temporal-YOLOv8.

Deep learning has become the preferred method for automated object detection, but the accurate detection of small objects remains a challenge due to the lack of distinctive appearance features. Most deep learning-based detectors do not exploit the temporal information that is available in video, even though this context is often essential when the signal-to-noise ratio is low. In addition, model development choices, such as the loss function, are typically designed around medium-sized objects.

[A man with polyarthralgia].

A 46-year old Ghanaian man presented with immobilizing arthralgias in all joints except the forefoot, with a periarticular swelling most pronounced around the wrists and ankles. He had erythema nodosum and hilar lymphadenopathy on chest radiograph. We diagnosed Löfgren syndrome.

Differences Between MR Brain Region Segmentation Methods: Impact on Single-Subject Analysis.

For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data.

Predicting Global Cognitive Decline in the General Population Using the Disease State Index.

Background: Identifying persons at risk for cognitive decline may aid in early detection of persons at risk of dementia and to select those that would benefit most from therapeutic or preventive measures for dementia.

Objective: In this study we aimed to validate whether cognitive decline in the general population can be predicted with multivariate data using a previously proposed supervised classification method: Disease State Index (DSI).

Methods: We included 2,542 participants, non-demented and without mild cognitive impairment at baseline, from the population-based Rotterdam Study (mean age 60.

Normative brain volumetry derived from different reference populations: impact on single-subject diagnostic assessment in dementia.

Brain imaging data are increasingly made publicly accessible, and volumetric imaging measures derived from population-based cohorts may serve as normative data for individual patient diagnostic assessment. Yet, these normative cohorts are usually not a perfect reflection of a patient's base population, nor are imaging parameters such as field strength or scanner type similar. In this proof of principle study, we assessed differences between reference curves of subcortical structure volumes of normal controls derived from two population-based studies and a case-control study.