首页|Massachusetts General Hospital and Harvard Medical School Reports Findings in Ma chine Learning (No code machine learning: validating the approach on use-case fo r classifying clavicle fractures)
Massachusetts General Hospital and Harvard Medical School Reports Findings in Ma chine Learning (No code machine learning: validating the approach on use-case fo r classifying clavicle fractures)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Boston, Unit ed States, by NewsRx correspondents, research stated, "We created an infrastruct ure for no code machine learning (NML) platform for non-programming physicians t o create NML model. We tested the platform by creating an NML model for classify ing radiographs for the presence and absence of clavicle fractures." Our news editors obtained a quote from the research from Massachusetts General H ospital and Harvard Medical School, "Our IRB-approved retrospective study includ ed 4135 clavicle radiographs from 2039 patients (mean age 52 ± 20 years, F:M 102 2:1017) from 13 hospitals. Each patient had two-view clavicle radiographs with a xial and anterior-posterior projections. The positive radiographs had either dis placed or non-displaced clavicle fractures. We configured the NML platform to au tomatically retrieve the eligible exams using the series' unique identification from the hospital virtual network archive via web access to DICOM Objects. The p latform trained a model until the validation loss plateaus. Once the testing was complete, the platform provided the receiver operating characteristics curve an d confusion matrix for estimating sensitivity, specificity, and accuracy. The NM L platform successfully retrieved 3917 radiographs (3917/4135, 94.7 % ) and parsed them for creating a ML classifier with 2151 radiographs in the trai ning, 100 radiographs for validation, and 1666 radiographs in testing datasets ( 772 radiographs with clavicle fracture, 894 without clavicle fracture). The netw ork identified clavicle fracture with 90 % sensitivity, 87 % specificity, and 88 % accuracy with AUC of 0.95 (confidence interv al 0.94-0.96)."
BostonUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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