Eui Jin Hwang1, Jung Hee Hong1, Kyung Hee Lee2, Jung Im Kim3, Ju Gang Nam1, Da Som Kim1, Hyewon Choi1, Seung Jin Yoo1, Jin Mo Goo1, Chang Min Park4. 1. Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. 2. Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea. 3. Department of Radiology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-gu, Seoul, 05278, South Korea. 4. Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. cmpark.morphius@gmail.com.
Abstract
OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection of post-biopsy pneumothorax in chest radiographs (CRs), in consecutive cohorts reflecting actual clinical situation. METHODS: We retrospectively included post-biopsy CRs of 1757 consecutive patients (1055 men, 702 women; mean age of 65.1 years) undergoing percutaneous lung biopsies from three institutions. A commercially available deep learning algorithm analyzed each CR to identify pneumothorax. We compared the performance of the algorithm with that of radiology reports made in the actual clinical practice. We also conducted a reader study, in which the performance of the algorithm was compared with those of four radiologists. Performances of the algorithm and radiologists were evaluated by area under receiver operating characteristic curves (AUROCs), sensitivity, and specificity, with reference standards defined by thoracic radiologists. RESULTS: Pneumothorax occurred in 17.5% (308/1757) of cases, out of which 16.6% (51/308) required catheter drainage. The AUROC, sensitivity, and specificity of the algorithm were 0.937, 70.5%, and 97.7%, respectively, for identification of pneumothorax. The algorithm exhibited higher sensitivity (70.2% vs. 55.5%, p < 0.001) and lower specificity (97.7% vs. 99.8%, p < 0.001), compared with those of radiology reports. In the reader study, the algorithm exhibited lower sensitivity (77.3% vs. 81.8-97.7%) and higher specificity (97.6% vs. 81.7-96.0%) than the radiologists. CONCLUSION: The deep learning algorithm appropriately identified pneumothorax in post-biopsy CRs in consecutive diagnostic cohorts. It may assist in accurate and timely diagnosis of post-biopsy pneumothorax in clinical practice. KEY POINTS: • A deep learning algorithm can identify chest radiographs with post-biopsy pneumothorax in multicenter consecutive cohorts reflecting actual clinical situation. • The deep learning algorithm has a potential role as a surveillance tool for accurate and timely diagnosis of post-biopsy pneumothorax.
OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection of post-biopsy pneumothorax in chest radiographs (CRs), in consecutive cohorts reflecting actual clinical situation. METHODS: We retrospectively included post-biopsy CRs of 1757 consecutive patients (1055 men, 702 women; mean age of 65.1 years) undergoing percutaneous lung biopsies from three institutions. A commercially available deep learning algorithm analyzed each CR to identify pneumothorax. We compared the performance of the algorithm with that of radiology reports made in the actual clinical practice. We also conducted a reader study, in which the performance of the algorithm was compared with those of four radiologists. Performances of the algorithm and radiologists were evaluated by area under receiver operating characteristic curves (AUROCs), sensitivity, and specificity, with reference standards defined by thoracic radiologists. RESULTS: Pneumothorax occurred in 17.5% (308/1757) of cases, out of which 16.6% (51/308) required catheter drainage. The AUROC, sensitivity, and specificity of the algorithm were 0.937, 70.5%, and 97.7%, respectively, for identification of pneumothorax. The algorithm exhibited higher sensitivity (70.2% vs. 55.5%, p < 0.001) and lower specificity (97.7% vs. 99.8%, p < 0.001), compared with those of radiology reports. In the reader study, the algorithm exhibited lower sensitivity (77.3% vs. 81.8-97.7%) and higher specificity (97.6% vs. 81.7-96.0%) than the radiologists. CONCLUSION: The deep learning algorithm appropriately identified pneumothorax in post-biopsy CRs in consecutive diagnostic cohorts. It may assist in accurate and timely diagnosis of post-biopsy pneumothorax in clinical practice. KEY POINTS: • A deep learning algorithm can identify chest radiographs with post-biopsy pneumothorax in multicenter consecutive cohorts reflecting actual clinical situation. • The deep learning algorithm has a potential role as a surveillance tool for accurate and timely diagnosis of post-biopsy pneumothorax.
Entities:
Keywords:
Artificial intelligence; Deep learning; Needle biopsy; Pneumothorax; Thoracic radiography
Authors: Jeong Hoon Lee; Jong Seok Ahn; Myung Jin Chung; Yeon Joo Jeong; Jin Hwan Kim; Jae Kwang Lim; Jin Young Kim; Young Jae Kim; Jong Eun Lee; Eun Young Kim Journal: Sensors (Basel) Date: 2022-07-02 Impact factor: 3.847
Authors: Catherine M Jones; Luke Danaher; Michael R Milne; Cyril Tang; Jarrel Seah; Luke Oakden-Rayner; Andrew Johnson; Quinlan D Buchlak; Nazanin Esmaili Journal: BMJ Open Date: 2021-12-20 Impact factor: 2.692
Authors: Johannes Rueckel; Christian Huemmer; Andreas Fieselmann; Florin-Cristian Ghesu; Awais Mansoor; Balthasar Schachtner; Philipp Wesp; Lena Trappmann; Basel Munawwar; Jens Ricke; Michael Ingrisch; Bastian O Sabel Journal: Eur Radiol Date: 2021-03-27 Impact factor: 5.315
Authors: Jong Seok Ahn; Shadi Ebrahimian; Shaunagh McDermott; Sanghyup Lee; Laura Naccarato; John F Di Capua; Markus Y Wu; Eric W Zhang; Victorine Muse; Benjamin Miller; Farid Sabzalipour; Bernardo C Bizzo; Keith J Dreyer; Parisa Kaviani; Subba R Digumarthy; Mannudeep K Kalra Journal: JAMA Netw Open Date: 2022-08-01
Authors: Yee Liang Thian; Dianwen Ng; James Thomas Patrick Decourcy Hallinan; Pooja Jagmohan; Soon Yiew Sia; Cher Heng Tan; Yong Han Ting; Pin Lin Kei; Geoiphy George Pulickal; Vincent Tze Yang Tiong; Swee Tian Quek; Mengling Feng Journal: Radiol Artif Intell Date: 2021-04-14