A Stacked Generalization Chest-X-Ray-Based Framework for Mispositioned Medical Tubes and Catheters Detection. (January 2023)

• Record Type: Journal Article
• Authors: Elaanba, Abdelfettah; Ridouani, Mohammed; Hassouni, Larbi
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Stacked Generalization -- Endotracheal tube -- Central venous catheter -- Nasogastric tube -- Chest X-ray image -- Convolutional networks -- Deep learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104111
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Automatic detection for mispositioned tubes and catheters. Reducing medical tubes and catheters positioning errors. Using the stacked generalization for tubes and catheters, abnormal positioning detection. Sensitivity of stacked generalization to the base learners number composing the stack. Abstract: Tubes and catheters are medical devices introduced into the human body to help ill patients in critical health conditions. However, several positioning errors occur during or after the placement of such devices (Endotracheal tubes mispositioned in 10 to 20% of intubations). In addition, the delay of X-ray diagnosis after surgery can cause serious complications. Such delays are caused by the hospitals' resourcelessness or due to workload in intensive care units. The X-rays images availability (Most used diagnosis modality in intensive care units, 40% to 50%) and the presence of tubes in those images (lines are present on 33% of X-ray images) present a fertile ground to feed DCNNs training on tube error detection tasks and reduce complications. However, training and tuning one DCNN learner to resolve tube detection is time-consuming. Therefore, we propose a custom stacked generalization framework to combine wake learners with a proposed meta learner neural network architecture to resolve tube error detection tasks. The proposed framework AUC (93.84%) outperforms other related work methods with the input size of (380pixel*380pixel). Furthermore, we demonstrated the sensibility of stacked generalization to the number of base learners. Moreover, we validated the utility of input cross-validation used to form level1-metadata for the stacked generalization. Our framework can be adapted to be integrated with a CAD (computer aid decision system) for tubes error detection. The CAD can detect errors immediately after patient screening and notify radiologists to prioritize diagnosis of cases with positioning errors to adjust tubes and reduce risks significantly.