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http://dx.doi.org/10.1007/s40336-016-0166-y | DOI Listing |
J Clin Med
January 2025
Department of Obstetrics and Gynaecology, Croydon University Hospital, Croydon CR7 7YE, UK.
The aim of this study is to validate a uniform method for measuring perineal descent which can be used for different imaging methods, to establish cut-off values for this measurement, and to assess diagnostic test accuracy (DTA) of imaging techniques using these cut-off values. Secondly, the study aims to correlate perineal descent to symptoms, signs and imaging findings in women with obstructed defaecation syndrome (ODS) to assess its clinical relevance. Cross-sectional study of 131 women with symptoms of ODS.
View Article and Find Full Text PDFTransl Psychiatry
January 2025
Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Advancements in single-cell multimodal techniques have greatly enhanced our understanding of disease-relevant loci identified through genome-wide association studies (GWASs). To investigate the biological connections between the eye and brain, we integrated bulk and single-cell multiomic profiles with GWAS summary statistics for eight neuropsychiatric and five ocular diseases. Our analysis uncovered five latent factors explaining 61.
View Article and Find Full Text PDFJ Imaging
January 2025
Department of Ophthalmology, General University Hospital of Alexandroupolis, 68131 Alexandroupolis, Greece.
Blink detection is considered a useful indicator both for clinical conditions and drowsiness state. In this work, we propose and compare deep learning architectures for the task of detecting blinks in video frame sequences. The first step is the training and application of an eye detector that extracts the eye regions from each video frame.
View Article and Find Full Text PDFJ Imaging
January 2025
Istituto di Scienze Applicate e Sistemi Intelligenti (ISASI), Consiglio Nazionale delle Ricerche (CNR), DHITECH, Campus Università del Salento, Via Monteroni s.n., 73100 Lecce, Italy.
Despite significant advancements in the automatic classification of skin lesions using artificial intelligence (AI) algorithms, skepticism among physicians persists. This reluctance is primarily due to the lack of transparency and explainability inherent in these models, which hinders their widespread acceptance in clinical settings. The primary objective of this study is to develop a highly accurate AI-based algorithm for skin lesion classification that also provides visual explanations to foster trust and confidence in these novel diagnostic tools.
View Article and Find Full Text PDFInt J Neural Syst
January 2025
Alibaba Cloud, Hangzhou, P. R. China.
Multi-label zero-shot learning (ML-ZSL) strives to recognize all objects in an image, regardless of whether they are present in the training data. Recent methods incorporate an attention mechanism to locate labels in the image and generate class-specific semantic information. However, the attention mechanism built on visual features treats label embeddings equally in the prediction score, leading to severe semantic ambiguity.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!