A clinical evaluation study of cardiothoracic ratio measurement using artificial intelligence

Pairash Saiviroonporn; Suwimon Wonglaksanapimon; Warasinee Chaisangmongkon; Isarun Chamveha; Pakorn Yodprom; Krittachat Butnian; Thanogchai Siriapisith; Trongtum Tongdee

doi:10.1186/s12880-022-00767-9

A clinical evaluation study of cardiothoracic ratio measurement using artificial intelligence

BMC Med Imaging. 2022 Mar 16;22(1):46. doi: 10.1186/s12880-022-00767-9.

Authors

Pairash Saiviroonporn¹, Suwimon Wonglaksanapimon², Warasinee Chaisangmongkon³, Isarun Chamveha⁴, Pakorn Yodprom¹, Krittachat Butnian¹, Thanogchai Siriapisith¹, Trongtum Tongdee¹

Affiliations

¹ Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand.
² Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand. wpsuwimon@gmail.com.
³ Institute of Field Robotics, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
⁴ Perceptra Co., Ltd., Bangkok, Thailand.

Abstract

Background: Artificial intelligence, particularly the deep learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on chest X-ray (CXR) images. In everyday clinical use, however, this technology is usually implemented in a non-automated (AI-assisted) capacity because it still requires approval from radiologists. We investigated the performance and efficiency of our recently proposed models for the AI-assisted method intended for clinical practice.

Methods: We validated four proposed DL models (AlbuNet, SegNet, VGG-11, and VGG-16) to find the best model for clinical implementation using a dataset of 7517 CXR images from manual operations. These models were investigated in single-model and combined-model modes to find the model with the highest percentage of results where the user could accept the results without further interaction (excellent grade), and with measurement variation within ± 1.8% of the human-operating range. The best model from the validation study was then tested on an evaluation dataset of 9386 CXR images using the AI-assisted method with two radiologists to measure the yield of excellent grade results, observer variation, and operating time. A Bland-Altman plot with coefficient of variation (CV) was employed to evaluate agreement between measurements.

Results: The VGG-16 gave the highest excellent grade result (68.9%) of any single-model mode with a CV comparable to manual operation (2.12% vs 2.13%). No DL model produced a failure-grade result. The combined-model mode of AlbuNet + VGG-11 model yielded excellent grades in 82.7% of images and a CV of 1.36%. Using the evaluation dataset, the AlbuNet + VGG-11 model produced excellent grade results in 77.8% of images, a CV of 1.55%, and reduced CTR measurement time by almost ten-fold (1.07 ± 2.62 s vs 10.6 ± 1.5 s) compared with manual operation.

Conclusion: Due to its excellent accuracy and speed, the AlbuNet + VGG-11 model could be clinically implemented to assist radiologists with CTR measurement.

Keywords: AI; CXR; Cardiothoracic ratio; Clinical evaluation; Deep learning.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Humans
Observer Variation
Radiologists
Thorax*