Acil Serviste Travmatik Torasik Acillerin Değerlendirilmesinde Yapay Zekâ Destekli Direkt Akciğer Grafisinin Acil Tıp ve Göğüs Cerrahisi Açısından Önemi
References
American College of Surgeons Committee on Trauma. (2008). Advanced Trauma Life Support for Doctors-ATLS: Student Course Manual. American College of Surgeons.
Caputo, G., Meda, S., Piccioni, A., Saviano, A., Ojetti, V., Savioli, G., Bavestrello Piccini, G., Ferrari, C., Voza, A., Pellegrini, L., Ottaviani, M., Spadazzi, F., Volonnino, G., & La Russa,
R. (2024). Thoracic trauma: Current approach in emergency medicine. Clinics and Practice, 14(5), 1869–1885. https://doi.org/10.3390/clinpract14050148
Gianola, S., Bargeri, S., Biffi, A., Cimbanassi, S., D’Angelo, D., Coclite, D., ... & Italian National Institute of Health guideline working group on Major Trauma. (2023). Structured approach with primary and secondary survey for major trauma care: an overview of reviews. World Jour- nal of Emergency Surgery, 18(1), 2.
Platz, J. J., Fabricant, L., & Norotsky, M. (2017). Thoracic trauma: injuries, evaluation, and tre- atment. Surgical Clinics, 97(4), 783-799.
Mistry, R. N., & Moore, J. E. (2022). Management of blunt thoracic trauma. BJA education, 22(11), 432-439.
Coccolini, F., Cremonini, C., Moore, E. E., Civil, I., Balogh, Z., Leppaniemi, A., ... & Coimb- ra, R. (2025). Thoracic trauma WSES-AAST guidelines. World Journal of Emergency Surgery, 20(1), 78.
Glaser, J. J., & Rodriguez, C. J. (2022). Open Chest Wounds and Flail Chest. In Atlas of Emer- gency Medicine Procedures (pp. 141-143). Cham: Springer International Publishing.
Mouawad, N. J., Paulisin, J., Hofmeister, S., & Thomas, M. B. (2020). Blunt thoracic aortic in- jury–concepts and management. Journal of Cardiothoracic Surgery, 15(1), 62.
NG39, N. G. (2016). Major trauma: assessment and initial management. Found at: www nice org uk/guidance/ng39/evidence/full-guideline-2308122833 Pages, 236-9.
Yamamoto, L., Schroeder, C., Morley, D., & Beliveau, C. (2005). Thoracic trauma: the deadly dozen. Critical Care Nursing Quarterly, 28(1), 22-40.
Ahn, J. S., Ebrahimian, S., McDermott, S., Lee, S., Naccarato, L., Di Capua, J. F., ... & Kalra,
M. K. (2022). Association of artificial intelligence–aided chest radiograph interpretation with reader performance and efficiency. JAMA Network Open, 5(8), e2229289.
Akgul Ozmen, C., Onat, S., & Aycicek, D. (2017). Radiologic findings of thoracic trauma. The- rapeutics and clinical risk management, 1085-1089.
Broder, J. S. (2011). Diagnostic Imaging for the Emergency Physician E-Book: Diagnostic Ima- ging for the Emergency Physician E-Book. Elsevier Health Sciences.
Gefter, W. B., & Hatabu, H. (2023). Reducing errors resulting from commonly missed chest radiography findings. Chest, 163(3), 634-649.
Kaviani, P., Kalra, M. K., Digumarthy, S. R., Gupta, R. V., Dasegowda, G., Jagirdar, A., ... & Dre- yer, K. J. (2022). Frequency of missed findings on chest radiographs (CXRs) in an international, multicenter study: application of AI to reduce missed findings. Diagnostics, 12(10), 2382.
Nousiainen, K., Mäkelä, T., Piilonen, A., & Peltonen, J. I. (2021). Automating chest radiograph imaging quality control. Physica Medica, 83, 138-145.
Park, I. H., Kim, C. W., Choi, Y. U., Kang, T. W., Lim, J., & Byun, C. S. (2023). Occult pneumot- horax in patients with blunt chest trauma: key findings on supine chest radiography. Journal of Thoracic Disease, 15(8), 4379.
Annarumma, M., Withey, S. J., Bakewell, R. J., Pesce, E., Goh, V., & Montana, G. (2019). Au- tomated triaging of adult chest radiographs with deep artificial neural networks. Radiology, 291(1), 196-202.
Baltruschat, I., Steinmeister, L., Nickisch, H., Saalbach, A., Grass, M., Adam, G., ... & Ittrich, H. (2021). Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation. European radiology, 31(6), 3837-3845.
Bennani, S., Regnard, N. E., Ventre, J., Lassalle, L., Nguyen, T., Ducarouge, A., ... & Chassagnon,
G. (2023). Using AI to improve radiologist performance in detection of abnormalities on chest radiographs. Radiology, 309(3), e230860.
Qin, C., Yao, D., Shi, Y., & Song, Z. (2018). Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomedical engineering online, 17(1), 113.
Syed, A. B., & Zoga, A. C. (2018, November). Artificial intelligence in radiology: current tech- nology and future directions. In Seminars in musculoskeletal radiology (Vol. 22, No. 05, pp. 540-545). Thieme medical publishers.
Yasaka, K., & Abe, O. (2018). Deep learning and artificial intelligence in radiology: Current applications and future directions. PLoS medicine, 15(11), e1002707.
Hillis, J. M., Bizzo, B. C., Mercaldo, S., Chin, J. K., Newbury-Chaet, I., Digumarthy, S. R., ... & Dreyer, K. J. (2022). Evaluation of an artificial intelligence model for detection of pneumotho- rax and tension pneumothorax in chest radiographs. JAMA Network Open, 5(12), e2247172.
Juluru, K., Shih, H.-H., Keshava Murthy, K. N., Elnajjar, P., El-Rowmeim, A., Roth, C., Gene- reaux, B., Fox, J., Siegel, E., & Rubin, D. L. (2021). Integrating AI algorithms into the clinical workflow. Radiology: Artificial Intelligence, 3(6), e210013.
Lee, K., Lee, S., Kwak, J. S., Park, H., Oh, H., & Koh, J. C. (2024). Development and validation of an artificial intelligence model for detecting rib fractures on chest radiographs. Journal of Clinical Medicine, 13(13), 3850.
Mongan, J., Moy, L., & Kahn Jr, C. E. (2020). Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiology: Artificial Intelligence, 2(2), e200029.
Park, S. H., Han, K., Jang, H. Y., Park, J. E., Lee, J. G., Kim, D. W., & Choi, J. (2023). Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis. Radiology, 306(1), 20-31.
Wong, K. P., Homer, S. Y., Wei, S. H., Yaghmai, N., Estrada Paz, O. A., Young, T. J., ... & Brown,
M. S. (2023). Integration and evaluation of chest X-ray artificial intelligence in clinical practice. Journal of Medical Imaging, 10(5), 051805-051805.
Zhou, L., Yin, X., Zhang, T., Feng, Y., Zhao, Y., Jin, M., ... & Lu, L. (2021). Detection and semiqu- antitative analysis of cardiomegaly, pneumothorax, and pleural effusion on chest radiographs. Radiology: Artificial Intelligence, 3(4), e200172.
Sugibayashi, T., Walston, S. L., Matsumoto, T., Mitsuyama, Y., Miki, Y., & Ueda, D. (2023). Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis. European Respi- ratory Review, 32(168), 220259.
Lind Plesner, L., Müller, F. C., Brejnebøl, M. W., Laustrup, L. C., Rasmussen, F., Nielsen, O. W.,
... & Brun Andersen, M. (2023). Commercially available chest radiograph AI tools for detecting airspace disease, pneumothorax, and pleural effusion. Radiology, 308(3), e231236.
Rahimi-Movaghar, V., Yousefifard, M., Ghelichkhani, P., Baikpour, M., Tafakhori, A., Asady, H., ... & Safari, S. (2016). Application of ultrasonography and radiography in detection of he- mothorax; a systematic review and meta-analysis. Emergency, 4(3), 116.
Schild, H. H., Strunk, H., Weber, W., Stoerkel, S., Doll, G., Hein, K., & Weitz, M. (1989). Pul- monary contusion: CT vs plain radiograms. Journal of computer assisted tomography, 13(3), 417-420.
van den Broek, M. C., Buijs, J. H., Schmitz, L. F., & Wijffels, M. M. (2024). Diagnostic perfor- mance of artificial intelligence in rib fracture detection: systematic review and meta-analysis. Surgeries, 5(1), 24-36.
Lee, W. S., Chong, V. E., & Victorino, G. P. (2015). Computed tomographic findings and morta- lity in patients with pneumomediastinum from blunt trauma. JAMA surgery, 150(8), 757-762.
Chang, J., Lin, B. R., Wang, T. H., & Chen, C. M. (2024). Deep learning model for pleural effusi- on detection via active learning and pseudo-labeling: a multisite study. BMC Medical Imaging, 24(1), 92.
Hunter, J. G., Bera, K., Shah, N., Bukhari, S. M. A., Marshall, C., Caovan, D., ... & Gupta, A. (2025). Real-world performance of pneumothorax-detecting artificial intelligence algorithm and its impact on radiologist reporting times. Academic radiology, 32(3), 1165-1174.
López Alcolea, J., Fernández Alfonso, A., Cano Alonso, R., Álvarez Vázquez, A., Díaz Moreno, A., García Castellanos, D., ... & Martínez de Vega, V. (2024). Diagnostic performance of arti- ficial intelligence in chest radiographs referred from the emergency department. Diagnostics, 14(22), 2592.
Katzman, B. D., Alabousi, M., Islam, N., Zha, N., & Patlas, M. N. (2024). Deep learning for pneumothorax detection on chest radiograph: a diagnostic test accuracy systematic review and meta analysis. Canadian Association of Radiologists Journal, 75(3), 525-533.
Huang, S. T., Liu, L. R., Chiu, H. W., Huang, M. Y., & Tsai, M. F. (2023). Deep convolutional neural network for rib fracture recognition on chest radiographs. Frontiers in Medicine, 10, 1178798.
Deng, L. Y., Lim, X. Y., Luo, T. Y., Lee, M. H., & Lin, T. C. (2023). Application of deep learning techniques for detection of pneumothorax in chest radiographs. Sensors, 23(17), 7369.
Langdorf, M. I., Medak, A. J., Hendey, G. W., Nishijima, D. K., Mower, W. R., Raja, A. S., ... & Rodriguez, R. M. (2015). Prevalence and clinical import of thoracic injury identified by chest computed tomography but not chest radiography in blunt trauma: multicenter prospective co- hort study. Annals of emergency medicine, 66(6), 589-600.
Staub, L. J., Biscaro, R. R. M., Kaszubowski, E., & Maurici, R. (2018). Chest ultrasonography for the emergency diagnosis of traumatic pneumothorax and haemothorax: a systematic review and meta-analysis. Injury, 49(3), 457-466.
Hwang, E. J., Goo, J. M., Nam, J. G., Park, C. M., Hong, K. J., & Kim, K. H. (2023). Conventional versus artificial intelligence-assisted interpretation of chest radiographs in patients with acute respiratory symptoms in emergency department: a pragmatic randomized clinical trial. Korean Journal of Radiology, 24(3), 259.
Rhee, C., Hong, K. J., Kim, K. H., Goo, J. M., & Hwang, E. J. (2025). Artificial intelligence analy- sis of chest radiographs for predicting major adverse events in patients visiting the emergency department with acute cardiopulmonary symptoms. Korean Journal of Radiology, 26(9), 877.
Arasteh, S. T., Ziller, A., Kuhl, C., Makowski, M., Nebelung, S., Braren, R., ... & Kaissis, G. (2023). Private, fair and accurate: Training large-scale, privacy-preserving ai models in medical imaging. arXiv preprint arXiv:2302.01622.
Liu, X., et al. (2023). Effect of human-AI interaction on detection of malignant lung nodules on chest radiographs. Radiology, 308(2), e222976. https://doi.org/10.1148/radiol.222976
Thian, Y. L., Ng, D., Hallinan, J. T. P. D., Jagmohan, P., Sia, S. Y., Tan, C. H., ... & Feng, M. (2021). Deep learning systems for pneumothorax detection on chest radiographs: a multicenter exter- nal validation study. Radiology: Artificial Intelligence, 3(4), e200190.
Yu, A. C., Mohajer, B., & Eng, J. (2022). External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiology: Artificial Intelligence, 4(3), e210064.
Khader, F., Müller-Franzes, G., Wang, T., Han, T., Tayebi Arasteh, S., Haarburger, C., ... & Truhn,
D. (2023). Multimodal deep learning for integrating chest radiographs and clinical parameters: a case for transformers. Radiology, 309(1), e230806.
Cheng, C. T., Ooyang, C. H., Liao, C. H., & Kang, S. C. (2025). Applications of deep learning in trauma radiology: a narrative review. Biomedical Journal, 48(1), 100743.
Yang, C., Zhao, H., Wang, A., Li, J., & Gao, J. (2024). Comparison of lung ultrasound assisted by artificial intelligence to radiology examination in pneumothorax. Journal of Clinical Ultra- sound, 52(8), 1051-1055.