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- W4312557024 abstract "<b>Introduction:</b> Chest radiographs remain the most frequently advised and often the first-line imaging investigation for diagnosis of suspected respiratory disorders. The incidence of a pulmonary nodule in the general population on chest radiographs is a low 0.2%. As a result, most radiologists and clinicians alike are not even looking for it or miss it. However, a nodule can have a variety of etiological causes and in cases the cause is malignant, then there is a high likelihood that the patient will present later with a more widely spread neoplasia, thus impacting both patient morbidity and mortality negatively. We developed a deep learning algorithm to diagnose pulmonary nodules and furthermore compared the actual impact it had on the performance of consultant clinicians for diagnosing the disease. <b>Methods:</b> The model is an ensemble of two FPN with Xception encoder, trained on 5325 radiographs and externally validated on 310 radiographs. Our study group encompassed- 4 consultant clinicians from separate fields, 3 radiology consultants & 5 residents. They participated in an exercise to detect pulmonary nodules unaided and aided by the predictions of the model. <b>Results:</b> The standalone AI has a specificity of 88% (83-92%) and sensitivity (CI) of 78% (69-85%). With the help of AI, the entire cohort’s Cohen Kappa score went up from the mean±SD of 58±17 to 66±8. The AUC improved from 0.79 to 0.83, with the greatest improvement seen in clinicians, increasing from 0.74 to 0.85. <b>Conclusion:</b> The performance of clinicians to detect nodules improved significantly when they were provided the algorithmic aid, thus proving its clinical utility." @default.
- W4312557024 created "2023-01-05" @default.
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- W4312557024 date "2022-09-04" @default.
- W4312557024 modified "2023-10-15" @default.
- W4312557024 title "Evaluating the effectivity of deploying a deep learning-based algorithm to aid clinicians and radiologists in diagnosing pulmonary nodules on chest radiographs" @default.
- W4312557024 doi "https://doi.org/10.1183/13993003.congress-2022.4308" @default.
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