Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

Abstract

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

“…These studies also recommended the use of CDSS in future research. Finally, 68 articles met all the inclusion criteria 5,17,34–99 . The flowchart of the selection process is shown in Figure 1.…”

COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest X-ray Images

“…While the initiative has been successful and leveraged globally, the continuously evolving nature of the pandemic and the increasing quantity of available CXR data from multinational cohorts has led to a growing demand for ever-improving computer-aided diagnostic solutions as part of the initiative. Since the launch of the COVID-Net open source initiative, there have been many studies in the area of COVID-19 case detection using CXR images (22)(23)(24)(25)(26)(27)(28)(29) emphasizing appropriate data curation and training regimes (30)(31)(32), with many leveraging the open access datasets and open source deep neural networks made publicly available through this initiative (33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49).…”

Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images

“…BS-Net tested on 5000 CXR segmented images based on the UNet variant model with a 94% IoU score. Research work proposed by Gidde and Prasad [36] developed CovBaseAI explainable decision system by an ensemble of three deep learning models for COVID-19 diagnosis. Model validation is performed by 2 datasets having a corpus of 471 and 1401 for COVID-19/Normal CXR scans.…”

“…In addition, specifications of training parameters like an optimizer, learning rate, loss function [31] , and reason for selection of the best model are missing [54] . In the literature survey, all the studies involve two or more classes but few performed class-wise sensitivity analysis [35] for better behavior analysis of the model [36] for each of the classes. Despite class imbalance majority of studies uses categorical class entropy loss [36] that may prioritize the learning of the majority class [55] only.…”

“…In the literature survey, all the studies involve two or more classes but few performed class-wise sensitivity analysis [35] for better behavior analysis of the model [36] for each of the classes. Despite class imbalance majority of studies uses categorical class entropy loss [36] that may prioritize the learning of the majority class [55] only. In the proposed research work, we consider all these challenges to develop a final multi-task model improving interpretation and generalization by segmentation-based cropping, explainable diagnosis by infection quantification model, localization map by Grad-CAM, and class imbalance by the multilabel classification loss function.…”