The learning curve as a concept has been considered, discussed and debated in medical education and healthcare for over two decades. The precise usage has been recognised in surgical disciplines both broad specialties and sub-specialties. Rollin Daniel in his book stated that, rhinoplasty is the most difficult of all cosmetic operations for three reasons, (a) nasal anatomy is highly variable, (b) the procedure must correct form and function and (c) the final result must meet the patients expectations. With this in mind a study was carried on the perception of learning curve in rhinoplasty based on a surgeon questionnaire at Marien Hospital, Stuttgart, Germany under Prof. Gubisch. Aims of the study were, (1) to extract the perception of learning curve of Rhinoplasty from surgeons across a spectrum of experience, i.e. less experienced to experienced, (2) To calculate the perception of learning curve in rhinoplasty as for other surgical procedures i.e. minimum number, interquartile range, surgical time, accelerators, (3) To chart-out a road-map for a novice rhinoplasty surgeon for continued improvement in surgical skills and ability. The conclusion derived was the concept of learning curve in rhinoplasty cannot be applied to the operation of Septo-Rhinoplasty as a whole because the two factors i.e. interquartile range and minimum number to achieve proficiency have a wide range and cannot be generalized. It is thought that each type of Rhinoplasty should be dealt with separately and learning curve calculated accordingly, i.e. hump reduction, crooked nose and augmentation rhinoplasty.
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http://dx.doi.org/10.1007/s12070-017-1199-x | DOI Listing |
Patterns (N Y)
December 2024
Data Sciences and Artificial Intelligence Section, College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA.
The placenta is vital to maternal and child health but often overlooked in pregnancy studies. Addressing the need for a more accessible and cost-effective method of placental assessment, our study introduces a computational tool designed for the analysis of placental photographs. Leveraging images and pathology reports collected from sites in the United States and Uganda over a 12-year period, we developed a cross-modal contrastive learning algorithm consisting of pre-alignment, distillation, and retrieval modules.
View Article and Find Full Text PDFInfect Drug Resist
January 2025
Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People's Republic of China.
Background: Early differentiation between spinal tuberculosis (STB) and acute osteoporotic vertebral compression fracture (OVCF) is crucial for determining the appropriate clinical management and treatment pathway, thereby significantly impacting patient outcomes.
Objective: To evaluate the efficacy of deep learning (DL) models using reconstructed sagittal CT images in the differentiation of early STB from acute OVCF, with the aim of enhancing diagnostic precision, reducing reliance on MRI and biopsies, and minimizing the risks of misdiagnosis.
Methods: Data were collected from 373 patients, with 302 patients recruited from a university-affiliated hospital serving as the training and internal validation sets, and an additional 71 patients from another university-affiliated hospital serving as the external validation set.
J Med Imaging (Bellingham)
January 2025
The University of Tokyo Hospital, Department of Radiology, Tokyo, Japan.
Purpose: The prevalence of type 2 diabetes mellitus (T2DM) has been steadily increasing over the years. We aim to predict the occurrence of T2DM using mammography images within 5 years using two different methods and compare their performance.
Approach: We examined 312 samples, including 110 positive cases (developed T2DM after 5 years) and 202 negative cases (did not develop T2DM) using two different methods.
JAMIA Open
February 2025
Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46202, United States.
Objective: Measurement of health-related social needs (HRSNs) is complex. We sought to develop and validate computable phenotypes (CPs) using structured electronic health record (EHR) data for food insecurity, housing instability, financial insecurity, transportation barriers, and a composite-type measure of these, using human-defined rule-based and machine learning (ML) classifier approaches.
Materials And Methods: We collected HRSN surveys as the reference standard and obtained EHR data from 1550 patients in 3 health systems from 2 states.
Int Urol Nephrol
January 2025
Medical College, Qinghai University, Xining, 810016, People's Republic of China.
Objective: Using machine learning to construct a prediction model for the risk of diabetes kidney disease (DKD) in the American diabetes population and evaluate its effect.
Methods: First, a dataset of five cycles from 2009 to 2018 was obtained from the National Health and Nutrition Examination Survey (NHANES) database, weighted and then standardized (with the study population in the United States), and the data were processed and randomly grouped using R software. Next, variable selection for DKD patients was conducted using Lasso regression, two-way stepwise iterative regression, and random forest methods.
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