Purpose: To elucidate the learning curve for posterior segment diagnostic endoscopy (DE) based on the results of a self-trained (ST) and a supervised (SUP) vitreoretinal surgeon.
Methods: Retrospective review of medical records of DE performed between 2017 and 2023 by one ST and one SUP vitreoretinal surgeon at a tertiary eye care institute. Data were collected and the serial number of cases was plotted against the time taken for the procedure. A comparative regression plot was created for both the surgeons to know the slope of the learning curve. The start time was noted as that of attachment of the endoscope and the stop time was noted as the end of diagnostic evaluation. Procedures were divided into blocks of 10 cases each and the time taken for the procedures was calculated.
Results: Total of 106 eyes (58 by ST surgeon and 48 by SUP surgeon) were included. For ST surgeon, the time taken for the surgery correlated inversely (reduced sequentially) with the serial number of the case till the 20 case (correlation coefficient = -0.5, = .01), for SUP surgeon, the time taken for the surgery correlated inversely with the serial number of the case till the 10 case (correlation coefficient = -0.9, = <0.0001) and then stabilized. Neither of the groups had any adverse events.
Conclusion: About 20 cases for a self-trained and about 10 cases for a supervised vitreoretinal surgeon are required to get stable with DE. These observations have implications in creating a training module for DE with appropriate number of training cases.
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http://dx.doi.org/10.1080/08820538.2024.2373269 | DOI Listing |
Sci Rep
January 2025
Department of Orthopaedics, Traditional Chinese Medical Hospital of Gansu Province, Qilihe District, Guazhou Street 418, Lanzhou, 730050,, Gansu, China.
Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio.
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January 2025
Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Korea.
Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied.
View Article and Find Full Text PDFComput Biol Med
January 2025
Thai Nguyen University of Information and Communication Technology, Thai Nguyen City, Viet Nam. Electronic address:
Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and progression of various diseases, including liver, cardiac, pulmonary, and neurological disorders. However, identifying succinylation sites through experimental methods is often labor-intensive, costly, and technically challenging.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark.
Background: Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis is costly and inaccurate in low-income regions, often relying on complete blood count (CBC) tests. This study employs machine learning (ML) to classify α-thalassemia traits based on gender and CBC, exploring the effects of grouping silent- and non-carriers.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Urology, Health Science University Eskisehir City Health Application and Research Center, 26080 Eskisehir, Turkey.
To establish a machine learning (ML) model for predicting prostate biopsy outcomes using prostate-specific antigen (PSA) values, multiparametric magnetic resonance imaging (mpMRI) findings, and hematologic parameters. The medical records of the patients who had undergone a prostate biopsy were evaluated. Laboratory findings, mpMRI findings, and prostate biopsy results were collected.
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