Automatic Diagnosis Chest-X-Ray-Based-Framework for Semantic Segmentation fnd Placement Errors Detection of Catheters and Tubes

Abstract

In daily healthcare work routines, automatic diagnosis systems are essential. Human errors are very likely when working in those dangerous environments with a heavy workload and stress. One of the medical procedures where mistakes are risky and can result in severe complications if not caught in time is the task of positioning tubes and catheters. A type of tube is inserted for a patient as part of the tube placement procedure. The position of the installed tube is then determined by screening the patient. Waiting for a radiologist to confirm the diagnosis will delay the tube adjustment. Indeed, more complications may result from the tube adjustment delay or any potential diagnostic mistakes. Through this work, we propose a framework for diagnosis and validation for in-time tube placement error detection. The framework analyzes the chest X-ray right after the tube is inserted and generates a segmentation mask along with classification values for possible errors. Our proposed framework is founded on a customized U-net model that provides competitive segmentation results (dice coefficient of 94,5%) compared to the original U-Net model version. Moreover, the proposed framework is optimized to support deployment on production-edge mobile devices with 75% fewer training parameters.