{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T19:16:33Z","timestamp":1789672593692,"version":"4.0.1"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T00:00:00Z","timestamp":1610409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Hollow organ perforation can precipitate a life-threatening emergency due to peritonitis followed by fulminant sepsis and fatal circulatory collapse. Pneumoperitoneum is typically detected as subphrenic free air on frontal chest X-ray images; however, treatment is reliant on accurate interpretation of radiographs in a timely manner. Unfortunately, it is not uncommon to have misdiagnoses made by emergency physicians who have insufficient experience or who are too busy and overloaded by multitasking. It is essential to develop an automated method for reviewing frontal chest X-ray images to alert emergency physicians in a timely manner about the life-threatening condition of hollow organ perforation that mandates an immediate second look. In this study, a deep learning-based approach making use of convolutional neural networks for the detection of subphrenic free air is proposed. A total of 667 chest X-ray images were collected at a local hospital, where 587 images (positive\/negative: 267\/400) were used for training and 80 images (40\/40) for testing. This method achieved 0.875, 0.825, and 0.889 in sensitivity, specificity, and AUC score, respectively. It may provide a sensitive adjunctive screening tool to detect pneumoperitoneum on images read by emergency physicians who have insufficient clinical experience or who are too busy and overloaded by multitasking.<\/jats:p>","DOI":"10.3390\/jcm10020254","type":"journal-article","created":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T14:28:53Z","timestamp":1610461733000},"page":"254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Deep Learning Method for Alerting Emergency Physicians about the Presence of Subphrenic Free Air on Chest Radiographs"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2946-3207","authenticated-orcid":false,"given":"Che-Yu","family":"Su","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsung-Yu","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4277-0623","authenticated-orcid":false,"given":"Cheng-Yen","family":"Tseng","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keng-Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan"},{"name":"Department of Physical Therapy, College of Health Science, Kaohsiung Medical University, Kaohsiung 80708, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi-Wei","family":"Lee","sequence":"additional","affiliation":[{"name":"Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1007\/s00268-003-6645-0","article-title":"Perforated peptic ulcer. Main factors of morbidity and mortality","volume":"27","author":"Nogueria","year":"2003","journal-title":"World J. Surg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1080\/110241599750006479","article-title":"Surgical approach and prognostic factors after peptic ulcer perforation","volume":"165","author":"Hermansson","year":"1999","journal-title":"Eur. J. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2338","DOI":"10.3748\/wjg.v9.i10.2338","article-title":"Significant factors associated with fatal outcome in emergency open surgery for perforated peptic ulcer","volume":"9","author":"Testini","year":"2003","journal-title":"World J. Gastroenterol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1097\/00000658-199408000-00008","article-title":"Adverse effects of delayed treatment for perforated peptic ulcer","volume":"220","author":"Svanes","year":"1994","journal-title":"Ann. Surg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.mcna.2007.12.004","article-title":"Gastrointestinal perforation and the acute abdomen","volume":"92","author":"Langell","year":"2008","journal-title":"Med. Clin. N. Am."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"574","DOI":"10.2214\/ajr.112.3.574","article-title":"The roentgenologic demonstration of tiny amounts of free intraperitoneal gas: Experimental and clinical studies","volume":"112","author":"Miller","year":"1971","journal-title":"Am. J. Roentgenol. Radium Ther. Nucl. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/S0196-0644(86)80180-9","article-title":"The lateral chest film and pneumoperitoneum","volume":"15","author":"Markowitz","year":"1986","journal-title":"Ann. Emerg. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/0002-9610(84)90467-7","article-title":"Pneumogastrography in the diagnosis of perforated peptic ulcer","volume":"148","author":"Maull","year":"1984","journal-title":"Am. J. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1016\/0002-9610(83)90353-7","article-title":"Value of pneumopeitoneum in the diagnosis of visceral perforation","volume":"146","author":"Roh","year":"1983","journal-title":"Am. J. Surg."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2214\/ajr.165.1.7785629","article-title":"Detection of pneumoperitoneum on chest radiographs: Comparison of upright lateral and posteroanterior projections","volume":"165","author":"Woodring","year":"1995","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1097\/00004728-199209000-00008","article-title":"Diagnosis of pneumoperitoneum: Abdominal CT vs. upright chest film","volume":"16","author":"Stapakis","year":"1992","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1097\/00004728-199405000-00018","article-title":"Pneumoperitoneum in peritoneal dialysis patients: Significance of diagnosis by CT","volume":"18","author":"Lee","year":"1994","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_13","first-page":"829","article-title":"Extraluminal air. Diagnosis and significance","volume":"32","author":"Cho","year":"1994","journal-title":"Radiol. Clin. N. Am."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"781","DOI":"10.2214\/ajr.161.4.8372757","article-title":"Prevalence and duration of postoperative pneumoperitoneum: Sensitivity of CT vs left lateral decubits radiography","volume":"161","author":"Earls","year":"1993","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Bengio","year":"2015","journal-title":"Nature"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Irvin, J.A., Ball, R.L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., and Langlotz, C. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002686"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Castro-Gutierrez, E., Estacio-Cerquin, L., Gallegos-Guillen, J., and Obando, J.D. (2019, January 4\u20136). Detection of Acetabulum Fractures Using X-Ray Imaging and Processing Methods Focused on Noisy Images. Proceedings of the 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, UAE.","DOI":"10.1109\/AICAI.2019.8701297"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Salehinejad, H., Valaee, S., Dowdell, T., and Barfett, J. (2018, January 15\u201320). Image Augmentation Using Radial Transform for Training Deep Neural Networks. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462241"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.zemedi.2018.12.003","article-title":"A gentle introduction to deep learning in medical image processing","volume":"29","author":"Maier","year":"2019","journal-title":"Z. Med. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u201310). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 1\u201326). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/S1076-6332(00)80117-8","article-title":"Emergency department coverage by academic departments of radiology","volume":"7","author":"Hunter","year":"2000","journal-title":"Acad. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/S0196-0644(88)80438-4","article-title":"Physician assessments of practice patterns in emergency department radiograph interpretation","volume":"17","author":"Smith","year":"1988","journal-title":"Ann. Emerg. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1136\/emj.8.4.266","article-title":"X-ray reporting in accident and emergency departments\u2014An area for improvements in efficiency","volume":"8","author":"James","year":"1991","journal-title":"Arch. Emerg. Med."},{"key":"ref_32","first-page":"160","article-title":"Emergency radiology in Canada: A national survey","volume":"53","author":"Torreggiani","year":"2002","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1056\/NEJMra072149","article-title":"Computed tomography\u2014An increasing source of radiation exposure","volume":"357","author":"Brenner","year":"2007","journal-title":"N. Engl. J. Med."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/10\/2\/254\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:10:04Z","timestamp":1760159404000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/10\/2\/254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,12]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["jcm10020254"],"URL":"https:\/\/doi.org\/10.3390\/jcm10020254","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,12]]}}}