Progress of rehabilitation in assisted living for mentally ill according to STAX-SA taxonomy.

Objective: The transition from institutional psychiatric care to community-based mental health services has resulted in the rapid development of assisted living services (AL) for mentally ill. Focus of the current study is to add internationally comparable evidence-based knowledge on the rehabilitation of AL residents by examining progression and mortality in relation to the level of service provided in AL units.

Methods: This study utilized data gathered from a longitudinal study conducted in Finland during the years 2020 to 2022.

Testing process for artificial intelligence applications in radiology practice.

Artificial intelligence (AI) applications are becoming increasingly common in radiology. However, ensuring reliable operation and expected clinical benefits remains a challenge. A systematic testing process aims to facilitate clinical deployment by confirming software applicability to local patient populations, practises, adherence to regulatory and safety requirements, and compatibility with existing systems.

Automatic chest computed tomography image noise quantification using deep learning.

Purpose: This study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions.

Methods: A comprehensive phantom CT dataset (three dose levels, six reconstruction methods, amounting to 9240 slices) was acquired and used to train a convolutional neural network (CNN) to output an estimate of local image noise standard deviations (SD) from a single CT scan input. The CNN model consisting of seven convolutional layers was trained on the phantom image dataset representing a range of scan parameters and was tested with phantom images acquired in a variety of different scan conditions, as well as publicly available chest CT images to produce clinical noise SD maps.

Assessing post-traumatic changes in cartilage using T dispersion parameters.

Degeneration of cartilage can be studied non-invasively with quantitative MRI. A promising parameter for detecting early osteoarthritis in articular cartilage is T, which can be tuned via the amplitude of the spin-lock pulse. By measuring T at several spin-lock amplitudes, the dispersion of T is obtained.

Machine Learning Prediction of Collagen Fiber Orientation and Proteoglycan Content From Multiparametric Quantitative MRI in Articular Cartilage.

Background: Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage.

Purpose: To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content.

Study Type: Retrospective, animal model.