Ju Gang Nam1,2, Jinwook Kim3, Keonwoo Noh3, Hyewon Choi1,2, Da Som Kim4, Seung-Jin Yoo5, Hyun-Lim Yang3, Eui Jin Hwang1,2, Jin Mo Goo1,2,6, Eun-Ah Park1,2,6, Hye Young Sun7, Min-Soo Kim8, Chang Min Park9,10,11,12. 1. Radiology, Seoul National University Hospital, Seoul, Republic of Korea. 2. College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea. 3. Information and Communication Engineering, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, 42988, Republic of Korea. 4. Radiology, Busan Paik Hospital and Inje University College of Medicine, Busan, 47392, Republic of Korea. 5. Radiology, Hanyang University Medical Center and College of Medicine, Seoul, 04763, Republic of Korea. 6. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, 03080, Republic of Korea. 7. Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, Republic of Korea. 8. School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea. 9. Radiology, Seoul National University Hospital, Seoul, Republic of Korea. cmpark.morphius@gmail.com. 10. College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea. cmpark.morphius@gmail.com. 11. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, 03080, Republic of Korea. cmpark.morphius@gmail.com. 12. Institute of Medical and Biological Engineering, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. cmpark.morphius@gmail.com.
Abstract
OBJECTIVE: To develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE), on chest radiographs METHODS: For training and internal validation of DLCE-LAE and -LVE, 5,045 chest radiographs (CRs; 2,463 normal and 2,393 LAE) and 1,012 CRs (456 normal and 456 LVE) matched with the same-day echocardiography were collected, respectively. External validation was performed using 107 temporally independent CRs. Reader performance test was conducted using the external validation dataset by five cardiothoracic radiologists without and with the results of DLCE. Classification performance of DLCE was evaluated and compared with those of the readers and conventional radiographic features, including cardiothoracic ratio, carinal angle, and double contour. In addition, DLCE-LAE was tested on 5,277 CRs from a healthcare screening program cohort. RESULTS: DLCE-LAE showed areas under the receiver operating characteristics curve (AUROCs) of 0.858 on external validation. On reader performance test, DLCE-LAE showed better results than pooled radiologists (AUROC 0.858 vs. 0.651; p < .001) and significantly increased their performance when used as a second reader (AUROC 0.651 vs. 0.722; p < .001). DLCE-LAE also showed a significantly higher AUROC than conventional radiographic findings (AUROC 0.858 vs. 0.535-0.706; all ps < .01). In the healthcare screening cohort, DLCE-LAE successfully detected 71.0% (142/200) CRs with moderate-to-severe LAE (93.5% [29/31] of severe cases), while yielding 11.8% (492/4,184) false-positive rate. DLCE-LVE showed AUROCs of 0.966 and 0.594 on internal and external validation, respectively. CONCLUSION: DLCE-LAE outperformed and improved cardiothoracic radiologists' performance in detecting LAE and showed promise in screening individuals with moderate-to-severe LAE in a healthcare screening cohort. KEY POINTS: • Our deep learning algorithm outperformed cardiothoracic radiologists in detecting left atrial enlargement on chest radiographs. • Cardiothoracic radiologists improved their performance in detecting left atrial enlargement when aided by the algorithm. • On a healthcare-screening cohort, our algorithm detected 71.0% (142/200) radiographs with moderate-to-severe left atrial enlargement while yielding 11.8% (492/4,184) false-positive rate.
OBJECTIVE: To develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE), on chest radiographs METHODS: For training and internal validation of DLCE-LAE and -LVE, 5,045 chest radiographs (CRs; 2,463 normal and 2,393 LAE) and 1,012 CRs (456 normal and 456 LVE) matched with the same-day echocardiography were collected, respectively. External validation was performed using 107 temporally independent CRs. Reader performance test was conducted using the external validation dataset by five cardiothoracic radiologists without and with the results of DLCE. Classification performance of DLCE was evaluated and compared with those of the readers and conventional radiographic features, including cardiothoracic ratio, carinal angle, and double contour. In addition, DLCE-LAE was tested on 5,277 CRs from a healthcare screening program cohort. RESULTS: DLCE-LAE showed areas under the receiver operating characteristics curve (AUROCs) of 0.858 on external validation. On reader performance test, DLCE-LAE showed better results than pooled radiologists (AUROC 0.858 vs. 0.651; p < .001) and significantly increased their performance when used as a second reader (AUROC 0.651 vs. 0.722; p < .001). DLCE-LAE also showed a significantly higher AUROC than conventional radiographic findings (AUROC 0.858 vs. 0.535-0.706; all ps < .01). In the healthcare screening cohort, DLCE-LAE successfully detected 71.0% (142/200) CRs with moderate-to-severe LAE (93.5% [29/31] of severe cases), while yielding 11.8% (492/4,184) false-positive rate. DLCE-LVE showed AUROCs of 0.966 and 0.594 on internal and external validation, respectively. CONCLUSION: DLCE-LAE outperformed and improved cardiothoracic radiologists' performance in detecting LAE and showed promise in screening individuals with moderate-to-severe LAE in a healthcare screening cohort. KEY POINTS: • Our deep learning algorithm outperformed cardiothoracic radiologists in detecting left atrial enlargement on chest radiographs. • Cardiothoracic radiologists improved their performance in detecting left atrial enlargement when aided by the algorithm. • On a healthcare-screening cohort, our algorithm detected 71.0% (142/200) radiographs with moderate-to-severe left atrial enlargement while yielding 11.8% (492/4,184) false-positive rate.
Entities:
Keywords:
Diagnosis, computer-assisted; Left atrium; X-ray film