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Review
. 2023 Jan 23;13(3):412.
doi: 10.3390/diagnostics13030412.

Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution

Giridhar Dasegowda  1   2 , Mannudeep K Kalra  1   2 , Alain S Abi-Ghanem  3 , Chiara D Arru  4 , Monica Bernardo  5   6 , Luca Saba  7 , Doris Segota  8 , Zhale Tabrizi  9 , Sanjaya Viswamitra  10 , Parisa Kaviani  1   2 , Lina Karout  1   2 , Keith J Dreyer  1   2
Affiliations
Review

Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution

Giridhar Dasegowda et al. Diagnostics (Basel). .

Abstract

Chest radiographs (CXR) are the most performed imaging tests and rank high among the radiographic exams with suboptimal quality and high rejection rates. Suboptimal CXRs can cause delays in patient care and pitfalls in radiographic interpretation, given their ubiquitous use in the diagnosis and management of acute and chronic ailments. Suboptimal CXRs can also compound and lead to high inter-radiologist variations in CXR interpretation. While advances in radiography with transitions to computerized and digital radiography have reduced the prevalence of suboptimal exams, the problem persists. Advances in machine learning and artificial intelligence (AI), particularly in the radiographic acquisition, triage, and interpretation of CXRs, could offer a plausible solution for suboptimal CXRs. We review the literature on suboptimal CXRs and the potential use of AI to help reduce the prevalence of suboptimal CXRs.

Keywords: artificial intelligence; chest X-ray; computer-assisted image processing; quality improvement; radiography.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Optimal and suboptimal chest X-rays. (A)—Optimal quality chest X-ray. Suboptimal chest X-rays (B–I) resulting from (B)—non-inclusion of lung apices and costophrenic angles; (C)—low lung volume/ inadequate inspiration; (D)—under-exposure; (E)—over-exposure; (F)—chin overlying the lung fields; (G)—patient rotation; (H)—foreign body(necklace) overlying the lung field; (I)—artifact in the lower part of the image obscuring part of left costophrenic angle.
Figure 2
Figure 2
CXRs and corresponding heat maps of four adult patients demonstrating various causes of suboptimal CXRs. The heat maps were produced by four of our AI models built on the Cognex Platform. (A) CXR with clipped left costophrenic angle was identified as red areas on heat map image A1; (B) Suboptimal CXR with non-inclusion of lung apices was marked in red color on heat map image B1; (C) Suboptimal CXR with clipped right costophrenic angle was identified as a red area on heat map image C1; (D) Suboptimal CXR due to patient’s chin obscuring lung and mediastinum was identified as a red region on heat map image D1.
See this image and copyright information in PMC

References

    1. Homayounieh F., Digumarthy S., Ebrahimian S., Rueckel J., Hoppe B.F., Sabel B.O., Conjeti S., Ridder K., Sistermanns M., Wang L., et al. An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study. JAMA Netw. Open. 2021;4:e2141096. doi: 10.1001/jamanetworkopen.2021.41096. - DOI - PMC - PubMed
    1. Suri J.S., Agarwal S., Carriero A., Paschè A., Danna P.S.C., Columbu M., Saba L., Viskovic K., Mehmedović A., Agarwal S., et al. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts. Diagnostics. 2021;11:2367. doi: 10.3390/diagnostics11122367. - DOI - PMC - PubMed
    1. Ebrahimian S., Digumarthy S., Bizzo B., Primak A., Zimmermann M., Tarbiah M.M., Kalra M., Dreyer K. Artificial Intelligence has Similar Performance to Subjective Assessment of Emphysema Severity on Chest CT. Acad. Radiol. 2022;29:1189–1195. doi: 10.1016/j.acra.2021.09.007. - DOI - PubMed
    1. Yoo H., Lee S.H., Arru C.D., Khera R.D., Singh R., Siebert S., Kim D., Lee Y., Park J.H., Eom H.J., et al. AI-based improvement in lung cancer detection on chest radiographs: Results of a multi-reader study in NLST dataset. Eur. Radiol. 2021;31:9664–9674. doi: 10.1007/s00330-021-08074-7. - DOI - PubMed
    1. Ebrahimian S., Kalra M., Agarwal S., Bizzo B., Elkholy M., Wald C., Allen B., Dreyer K. FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies. Acad. Radiol. 2022;29:559–566. doi: 10.1016/j.acra.2021.09.002. - DOI - PubMed

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