: Chronic leg ulcers present a global challenge in healthcare, necessitating precise wound measurement for effective treatment evaluation. This study is the first to validate the "split-wound design" approach for wound studies using objective measures. We further improved this relatively new approach and combined it with a semi-automated wound measurement algorithm. : The algorithm is capable of plotting an objective halving line that is calculated by splitting the bounding box of the wound surface along the longest side. To evaluate this algorithm, we compared the accuracy of the subjective wound halving of manual operators of different backgrounds with the algorithm-generated halving line and the ground truth, in two separate rounds. : The median absolute deviation (MAD) from the ground truth of the manual wound halving was 2% and 3% in the first and second round, respectively. On the other hand, the algorithm-generated halving line showed a significantly lower deviation from the ground truth (MAD = 0.3%, < 0.001). : The data suggest that this wound-halving algorithm is suitable and reliable for conducting wound studies. This innovative combination of a semi-automated algorithm paired with a unique study design offers several advantages, including reduced patient recruitment needs, accelerated study planning, and cost savings, thereby expediting evidence generation in the field of wound care. Our findings highlight a promising path forward for improving wound research and clinical practice.
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http://dx.doi.org/10.3390/jcm13123599 | DOI Listing |
BJUI Compass
December 2024
Desai Sethi Urology Institute, Miller School of Medicine University of Miami Miami Florida USA.
Objectives: The objectives of this study are to compare the accuracy of warm ischemia times (WITs) derived by a surgical artificial intelligence (AI) software to those documented in surgeon operative reports during partial nephrectomy procedures and to assess the potential of this technology in evaluating postoperative renal function.
Patients And Methods: A surgical AI software (Theator Inc., Palo Alto, CA) was used to capture and analyse videos of partial nephrectomies performed between October 2023 and April 2024.
Eur J Cancer
December 2024
Division of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address:
Purpose: Ovarian cancer patients with a Homologous Recombination Deficiency (HRD) often benefit from polyadenosine diphosphate-ribose polymerase (PARP) inhibitor maintenance therapy after response to platinum-based chemotherapy. HR status is currently analyzed via complex molecular tests. Predicting benefit from PARP inhibitors directly on histological whole slide images (WSIs) could be a fast and cheap alternative.
View Article and Find Full Text PDFSignals (Basel)
December 2024
Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.
Polysomnography (PSG) measures brain activity during sleep via electroencephalography (EEG) using six leads. Artifacts caused by movement or loose leads distort EEG measurements. We developed a method to automatically identify such artifacts in a PSG EEG trace.
View Article and Find Full Text PDFClin Ophthalmol
December 2024
Department of Ophthalmology, Cliniques Universitaires Saint Luc, UCL, Brussels, Belgium.
Purpose: This study aims to evaluate the inter-observer variability in assessing the optic disc in fundus photographs and its implications for establishing ground truth in AI research.
Methods: Seventy subjects were screened during a screening campaign. Fundus photographs were classified into normal (NL) or abnormal (GS: glaucoma and glaucoma suspects) by two masked glaucoma specialists.
Med Image Anal
December 2024
University Hospital Zurich and University of Zurich, Center for Translational and Experimental Cardiology, Zürich, Switzerland.
Transthoracic Echocardiography (TTE) is a crucial tool for assessing cardiac morphology and function quickly and non-invasively without ionising radiation. However, the examination is subject to intra- and inter-user variability and recordings are often limited to 2D imaging and assessments of end-diastolic and end-systolic volumes. We have developed a novel, fully automated machine learning-based framework to generate a personalised 4D (3D plus time) model of the left ventricular (LV) blood pool with high temporal resolution.
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