ArticleMedical & biological engineering & computing2022
Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 12 citations in OpenAlex.
- Perspectives on blastomycosis in Canada in the face of climate change.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024Article
- Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters.BMC pulmonary medicine · 2024Article
- Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.Health science reports · 2024Article
- Prediction of subsolid pulmonary nodule growth rate using radiomics.BMC medical imaging · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
Automatic computer-aided diagnosis (CAD) system has been widely used as an assisting tool for mass screening and risk assessment of infectious pulmonary diseases (PDs). However, such a system still lacks clinical acceptability and trust due to the integration gap between the patient's metadata, radiologist feedback, and the CAD system. This paper proposed three integration frameworks, namely-direct integration (DI), rule-based integration (RBI), and weight-based integration (WBI). The proposed framework helps clinicians diagnose lung inflammation and provide an end-to-end robust diagnostic system. Initially, the feasibility of integrating patients' symptoms, clinical pathologies, and radiologist feedback with CAD system to improve the classification performance is investigated. Subsequently, the patient's metadata and radiologist feedback are integrated with the CAD system using the proposed integration frameworks. The proposed method's performance is evaluated using a private dataset consisting of 70 chest X-ray (CXR) images (31 COVID-19, 14 other diseases, and 25 normal). The obtained results reveal that the proposed WBI achieved the highest classification performance (accuracy = 98.18%, F
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