No-code machine learning in radiology: implementation and validation of a platform that allows clinicians to train their own models

Abstract

ABSTRACT Machine learning models can assist clinicians and researchers in many tasks within radiology such as diagnosis, triage, segmentation/measurement, and quality assurance. To better leverage machine learning we have developed a platform that allows users to label data and train models without requiring any programming knowledge. The technology stack consists of a TypeScript web application running on .NET for user interaction, Python, PyTorch, and MONAI for machine learning, DICOM WADO-RS to retrieve data from clinical systems, and Docker for model management. As a first trial of the system, researchers used it to train a model for clavicle fracture detection as part of an IRB-approved retrospective study. The researchers labeled 4,135 clavicle radiographs from 2,039 patients across 13 sites. The platform automatically split the data into training, validation, and test sets and trained a model until the validation loss plateaued. The system then returned a receiver operating characteristic curve, AUC, F1, and other metrics. The resulting model identifies clavicle fractures with 90% sensitivity, 87% specificity, and 88% accuracy with an AUC of 0.95. This model performance is equivalent to or better than similar models reported in the literature. More recently, our system was used to train a model to identify if ultrasound frames that contain personally identifiable information (PII). After validation, the model was used to help de-identify a large dataset that was to be used for research. This first-of-its-kind system streamlines model development and deployment and opens up an exciting new pathway for the use of AI within healthcare.

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