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Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study

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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%, F1 score = 97.73%, and Matthew’s correlation coefficient = 0.969) compared to DI and RI. The generalization capability of the proposed framework is also verified from an external validation set. Furthermore, the Friedman average ranking and Shaffer and Holm post hoc statistical methods reveal the obtained results’ statistical significance.

Graphical abstract

Methodological diagram of proposed integration frameworks

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Notes

  1. Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, Chhattisgarh, India. Website: https://ptjnmcraipur.in/

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Acknowledgements

The authors thank Dr. Satyabhuwan Singh Netam and Dr. Deepak Jain, Department of Radiodiagnosis, Pt. Jawaharlal Nehru Memorial Medical College, Raipur (C.G.), India, and Dr. Sujit Kumar Samanta, Department of Mathematics, National Institute of Technology Raipur, Chhattisgarh, India for their valuable guidance and suggestions.

This study uses the chest X-ray images and the patient histopathological information collected from Pt. Jawaharlal Nehru Memorial Medical College, Raipur (C.G.), India. The necessary ethical permissions (Letter Dispatch No. 13767, Dated: 09/12/2020 and Ethical Approval No.: NITRR/IEC/2019/08, Dated: 12/09/2019) have been obtained prior to the data collection from the competent authorities of both organizations.

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Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology Raipur, Chhattisgarh, 492010, India

    Tej Bahadur Chandra

  2. Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

    Bikesh Kumar Singh

  3. Department of Radiodiagnosis, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, Chhattisgarh, India

    Deepak Jain

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Appendix

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Table 15

Table 15 The detailed description of the 22 GLCM features [47,48,49]
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Chandra, T.B., Singh, B.K. & Jain, D. Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study. Med Biol Eng Comput 60, 2549–2565 (2022). https://doi.org/10.1007/s11517-022-02611-2

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