On the basis of studying of data on 1469 patients there is proposed a methodology of development of a system of automated diagnosis of osteo-chondrodysplasia in children. It is based on registration of the course peculiarities, variety of clinical and genetic forms, inter- and intragroup similarity and progredient course of diseases of the type. At the first stage there have been made a complex analysis of formalized clinico-roentgenologic signs of osteo-chondrodysplasia with the aid of "Rbase-5000" program package. Osteo-chondrodysplasia course regularities, revealed in this way, were used further on in the process of an expert system development for their automated diagnosis.
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BMC Med Inform Decis Mak
January 2025
Department of Digital Systems, University of Piraeus, Piraeus, Greece.
Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and automated detection as well as quantitative assessment of skin diseases. This review provides a systematic literature search regarding the analysis of computer vision techniques applied to these benign skin conditions, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
View Article and Find Full Text PDFGraefes Arch Clin Exp Ophthalmol
January 2025
Experimental Psychology, Faculty of Social Sciences, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands.
Purpose: Assessing the quality of the visual field is important for the diagnosis of ophthalmic and neurological diseases and, consequently, for rehabilitation. Visual field defects (VFDs) are typically assessed using standard automated perimetry (SAP). However, SAP requires participants to understand instructions, maintain fixation and sustained attention, and provide overt responses.
View Article and Find Full Text PDFRMD Open
January 2025
Rheumatology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. In parallel, generative AI, driven by large language models, is becoming a powerful tool for optimising the research workflow by supporting with content generation, literature review automation and clinical decision support.
View Article and Find Full Text PDFAsian J Endosc Surg
January 2025
Department of Gastrointestinal Surgery, Institute of Science Tokyo, Tokyo, Japan.
Aim: Robotic total mesorectal excision (TME) with resection of adjacent organs has been increasingly used for locally advanced rectal cancer; however, few studies have focused on robotic TME with partial prostatectomy. Therefore, this study aimed to demonstrate the advantages of robotic TME with partial prostatectomy compared with open surgery for rectal cancer.
Method: This retrospective cohort study examined consecutive patients with rectal cancer who underwent robotic or open TME with partial prostatectomy at a high-volume center in Japan from April 2003 to March 2022.
J Med Internet Res
January 2025
Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center For Child Health, Hang Zhou, China.
Background: Accurate classification of patient complaints is crucial for enhancing patient satisfaction management in health care settings. Traditional manual methods for categorizing complaints often lack efficiency and precision. Thus, there is a growing demand for advanced and automated approaches to streamline the classification process.
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