Objective: This study aimed to assess whether increasing operative experience results in better surgical outcomes in endoscopic middle-ear surgery.
Methods: A retrospective single-institution cohort study was performed. Patients underwent endoscopic tympanoplasty between May 2013 and April 2019 performed by the senior surgeon or a trainee surgeon under direct supervision from the senior surgeon. Following data collection, statistical analysis compared success rates between early (learning curve) surgical procedures and later (experienced) tympanoplasties.
Results: In total, 157 patients (86 male, 71 female), with a mean age of 41.6 years, were included. The patients were followed up for an average of 43.2 weeks. The overall primary closure rate was 90.0 per cent.
Conclusion: This study demonstrates an early learning curve for endoscopic ear surgery that improves with surgical experience. Adoption of the endoscopic technique did not impair the success rates of tympanoplasty.
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http://dx.doi.org/10.1017/S002221512000078X | DOI Listing |
Brief Bioinform
November 2024
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005, Republic of Korea.
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.
View Article and Find Full Text PDFBreast Cancer Res
January 2025
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging in clinical practice. In this study, we validated the correlation between EMT status and resistance to endocrine therapy in ER+ breast cancer from a transcriptomic perspective.
View Article and Find Full Text PDFGlobal Spine J
January 2025
Department of Orthopaedics, Phramongkutklao Hospital and College of Medicine, Bangkok, Thailand.
Study Design: Systematic review.
Objective: Artificial intelligence (AI) and deep learning (DL) models have recently emerged as tools to improve fracture detection, mainly through imaging modalities such as computed tomography (CT) and radiographs. This systematic review evaluates the diagnostic performance of AI and DL models in detecting cervical spine fractures and assesses their potential role in clinical practice.
Open Heart
January 2025
Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
Am J Pathol
January 2025
Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA; Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA. Electronic address:
Grading activity of inflammatory bowel disease (IBD) using standardized histopathological scoring systems remains challenging due to limited availability of pathologists with IBD expertise and inter-observer variability. In this study, a deep learning model was developed to classify activity grades in hematoxylin and eosin-stained whole slide images (WSIs) from patients with IBD, offering a robust approach for general pathologists. This study utilized 2,077 WSIs from 636 patients who visited Dartmouth-Hitchcock Medical Center in 2018 and 2019, scanned at 40× magnification (0.
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