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Peripheral nerve injury induces dystonia-like movements and dysregulation in the energy metabolism: A multi-omics descriptive study in Thap1 mice.
Neurobiol Dis
December 2024
Department of Neurology, University Hospital of Wuerzburg, Germany. Electronic address:
DYT-THAP1 dystonia is a monogenetic form of dystonia, a movement disorder characterized by the involuntary co-contraction of agonistic and antagonistic muscles. The disease is caused by mutations in the THAP1 gene, although the precise mechanisms by which these mutations contribute to the pathophysiology of dystonia remain unclear. The incomplete penetrance of DYT-THAP1 dystonia, estimated at 40 to 60 %, suggests that an environmental trigger may be required for the manifestation of the disease in genetically predisposed individuals.
View Article and Find Full Text PDFMachine learning-based pipeline for automated intracerebral hemorrhage and drain detection, quantification, and classification in non-enhanced CT images (NeuroDrAIn).
PLoS One
December 2024
Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Background And Purpose: External drainage represents a well-established treatment option for acute intracerebral hemorrhage. The current standard of practice includes post-operative computer tomography imaging, which is subjectively evaluated. The implementation of an objective, automated evaluation of postoperative studies may enhance diagnostic accuracy and facilitate the scaling of research projects.
View Article and Find Full Text PDFWhy implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis.
EBioMedicine
December 2024
Department of Haematology & Stem Cell Transplantation, West German Cancer Center, University Hospital Essen, Essen, Germany; Laboratory for Clinical Research and Real-World Evidence, Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. Electronic address:
Article Synopsis
- The study evaluated the performance of the AI-PAL machine learning algorithm for diagnosing acute leukaemia when implemented in a real-world clinical setting at the University Hospital Essen.
- Results showed that the AI-PAL algorithm performed significantly worse in the clinical simulation compared to previous results, with key performance metrics falling below the expected levels.
- The findings highlight the necessity for local validation and potential recalibration of machine learning models before their use in clinical settings to ensure they are reliable and safe for patient care.
Ecologically sustainable benchmarking of AI models for histopathology.
NPJ Digit Med
December 2024
Institute of Pathology, University Clinic Aachen, RWTH Aachen University, Aachen, Germany.
Deep learning (DL) holds great promise to improve medical diagnostics, including pathology. Current DL research mainly focuses on performance. DL implementation potentially leads to environmental consequences but approaches for assessment of both performance and carbon footprint are missing.
View Article and Find Full Text PDFAnatomy-centred deep learning improves generalisability and progression prediction in radiographic sacroiliitis detection.
RMD Open
December 2024
Department of Gastroenterology, Infectious Diseases and Rheumatology (incl. Nutrition Medicine), Charite - Universitatsmedizin Berlin, Berlin, Germany.
Purpose: To examine whether incorporating anatomy-centred deep learning can improve generalisability and enable prediction of disease progression.
Methods: This retrospective multicentre study included conventional pelvic radiographs of four different patient cohorts focusing on axial spondyloarthritis collected at university and community hospitals. The first cohort, which consisted of 1483 radiographs, was split into training (n=1261) and validation (n=222) sets.
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