Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease : a feasibility study
© 2022. International Federation for Medical and Biological Engineering..
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. Methodological diagram of proposed integration frameworks.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:60 |
|---|---|
| Enthalten in: |
Medical & biological engineering & computing - 60(2022), 9 vom: 01. Sept., Seite 2549-2565 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chandra, Tej Bahadur [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Classification |
|---|
| Anmerkungen: |
Date Completed 12.08.2022 Date Revised 12.08.2022 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1007/s11517-022-02611-2 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM342994271 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM342994271 | ||
| 003 | DE-627 | ||
| 005 | 20231226015403.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11517-022-02611-2 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1143.xml |
| 035 | |a (DE-627)NLM342994271 | ||
| 035 | |a (NLM)35778670 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chandra, Tej Bahadur |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease |b a feasibility study |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 12.08.2022 | ||
| 500 | |a Date Revised 12.08.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022. International Federation for Medical and Biological Engineering. | ||
| 520 | |a 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. Methodological diagram of proposed integration frameworks | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Classification | |
| 650 | 4 | |a Computer-aided diagnosis | |
| 650 | 4 | |a Integrated CAD framework | |
| 650 | 4 | |a Integration model | |
| 650 | 4 | |a Patient symptomatology | |
| 650 | 4 | |a Radiological findings | |
| 700 | 1 | |a Singh, Bikesh Kumar |e verfasserin |4 aut | |
| 700 | 1 | |a Jain, Deepak |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Medical & biological engineering & computing |d 1993 |g 60(2022), 9 vom: 01. Sept., Seite 2549-2565 |w (DE-627)NLM000344060 |x 1741-0444 |7 nnns |
| 773 | 1 | 8 | |g volume:60 |g year:2022 |g number:9 |g day:01 |g month:09 |g pages:2549-2565 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/s11517-022-02611-2 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 60 |j 2022 |e 9 |b 01 |c 09 |h 2549-2565 | ||