Клинические аспекты применения искусственного интеллекта для интерпретации рентгенограмм органов грудной клетки

В обзоре рассмотрены возможности применения искусственного интеллекта для интерпретации рентгенограмм органов грудной клетки путем анализа 45 литературных источников. Проанализированы экспериментальные и коммерческие системы диагностики туберкулеза легких, пневмоний, новообразований и других заболеваний.

1. Морозов С. П., Владзимирский А. В., Кляшторный В. Г., Андрейченко А. Е., Кульберг Н. С., Гомболевский В. А., Сергунова К. А. Клинические испытания программного обеспечения на основе интеллектуальных технологий (лучевая диагностика). Серия «Лучшие практики лучевой и инструментальной диагностики». – Вып. 57. – М., 2019. – 51 с.

2. Apostolopoulos I. D., Mpesiana T. A. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks // Phys. Eng. Sci. Med. – 2020. – № 43. – P. 635-640.

3. Ather S., Kadir T., Gleeson F. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications // Clin. Radiol. – 2020. – № 75 (1). – P. 13-19.

4. Behzadi-Khormouji H., Rostami H., Salehi S. et al. Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images // Comput Methods Programs Biomed. – 2020. – № 185. ‒ Р. 105162.

5. Bush I. Lung Nodule Detection and Classification // Report, Stanford Computer Science. – 2016. – 8 p.

6. Cha M. J., Chung M. J., Lee J. H., Lee K. S. Performance of deep learning model in detecting operable lung cancer with chest radiographs // J. Thorac. Imaging. – 2019. – № 34 (2). – P. 86-91.

7. Chassagnon G., Vakalopoulou M., Paragios N., Revel M. P. Artificial intelligence applications for thoracic imaging // Eur. J. Radiol. – 2020. – № 23. ‒ Р. 108774.

8. Chest radiography in tuberculosis detection – summary of current WHO recommendations and guidance on programmatic approaches // World Health Organization. – 2016. – 39 p.

9. Del Ciello A., Franchi P., Contegiacomo A., Cicchetti G., Bonomo L., Larici A.R. Missed lung cancer: when, where, and why? // Diagn. Interv. Radiol. – 2017. – № 23 (2). – P. 118-126.

10. Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2016. Geneva, World Health Organization, 2018.

11. Global tuberculosis report 2019 // Geneva: World Health Organization, 2019. – 283 p.

12. Goutte C., Gaussier E. A Probabilistic interpretation of precision, recall and F-score, with Implication for evaluation // Lecture Notes in Computer Science. – 2005. – 3408. – P. 345-359.

13. Hogeweg L., Mol C., de Jong P. A., Dawson R., Ayles H., van Ginneken B. Fusion of local and global detection systems to detect tuberculosis in chest radiographs // Comput. Assist. Interv. – 2010. – № 13 (3). – P. 650-657.

14. Hwang E. J., Nam J. G., Lim W. H., Park S. J., Jeong Y. S., Kang J. H., Hong E. K., Kim T. M., Goo J. M., Park S., Kim K. H., Park C. M. et al. Deep Learning for Chest Radiograph Diagnosis in the Emergency Department // Radiology. – 2019. – № 293 (3). – P. 573-580.

15. Hwang E. J., Park S., Jin K. N. et al. Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs // JAMA Netw Open. – 2019. – № 2 (3). ‒ Р. 191095.

16. Hwang S., Kim H. E., Jeong J., Kim H. J. A novel approach for tuberculosis screening based on deep convolutional neural networks // Proc. SPIE. Medical Imaging. – 2016. – Р. 97852.

17. Jaeger S., Karargyris A., Antani S. et al. Detecting tuberculosis in radiographs using combined lung masks // In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE. – 2012. – P. 4978-4981.

18. Jaeger S., Karargyris A., Candemir S. et al. Automatic screening for tuberculosis in chest radiographs: a survey // Quant Imaging Med Surg. – 2013. – № 3 (2). – P. 89-99.

19. Jaeger S., Karargyris A., Candemir S., Folio L., Siegelman J., Callaghan F. M., Xue Z., Palaniappan K., Singh R. K., Antani S., Thoma G. R., Wang Y., Lu P., McDonald C. J. Automatic tuberculosis screening using chest radiographs // IEEE Trans. Med.Imaging. – 2014. – № 33 (2). – P. 233-245.

20. Kallianos K., Mongan J., Antani S. et al. How far have we come? Artificial intelligence for chest radiograph interpretation // Clin. Radiol. – 2019. – № 74 (5). – P. 338-345.

21. Khan A. I., Shah J. L., Bhat M. M. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images // Computer Methods and Programs in Biomedicine. – 2020. – 196.

22. Kulkarni S., Jha S. Artificial intelligence, radiology, and tuberculosis: a review // Acad Radiol. – 2020. – № 27 (1). – P. 71-75.

23. Lakhani P., Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks // Radiology. – 2017. – № 284 (2). – P.574-582.

24. Mahomed N., van Ginneken B., Philipsen R. H. H. M. et al. Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children // Pediatr Radiol. – 2020. – № 50 (4). – P. 482‐491.

25. Matsubara N., Teramoto A., Saito K. et al. Bone suppression for chest X-ray image using a convolutional neural filter // Phys Eng Sci Med. – 2020. – № 43. – P. 97-108.

26. Melendez J., Sanchez C.I., Philipsen R. H. H. M. et al. An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information // Sci. Rep. – 2016. – № 6. ‒ Р. 25265.

27. Murphy K., Habib S. S., Zaidi S. M. A. et al. Computer aided detection of tuberculosis on chest radiographs: an evaluation of the CAD4TB v6 system // Sci. Rep. – 2020. – № 10. ‒ Р. 5492.

28. Nam J. G., Park S., Hwang E. J., Lee J. H., Jin K.-N., Lim K. Y., Vu T. H., Sohn J. H., Hwang S., Goo J. M., Park C. M. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs // Radiology. – 2019. – 290. – P. 218-228.

29. Narin A., Kaya C., Pamuk Z. Automatic Detection of Coronavirus Disease (COVID-19) Using X-Ray Images and Deep Convolutional Neural Networks // arXiv preprint arXiv:2003.10849. 2020.

30. National Lung Screening Trial Research Team, Aberle D. R., Adams A. M., et al. Reduced lung-cancer mortality with low-dose computed tomographic screening // N. Engl. J. Med. – 2011. – № 365 (5). ‒ Р. 395-409.

31. Pande T., Cohen C., Pai M., Ahmad Khan F. Computer-aided detection of pulmonary tuberculosis on digital chest radiographs: a systematic review // Int. J. Tuberc. Lung Dis. – 2016. – № 20 (9). – P. 1226-1230.

32. Pasa F., Golkov V., Pfeiffer F. et al. Efficient deep network architectures for fast chest x-ray tuberculosis screening and visualization // Sci. Rep. – 2019. – № 9. – P. 62-68.

33. Putha P., Tadepalli M., Reddy B., Raj T., Chiramal J. A., Govil S., Sinha N., Reddivari S., Jagirdar A., Rao P., Warier P. Can Artificial intelligence reliably report chest X-Rays?: radiologist validation of an algorithm trained on 1.2 million X-Rays // arXiv preprint arXiv:1807.07455 (2018).

34. Qin C., Yao D., Shi Y., Song Z. Computer-aided detection in chest radiography based on artificial intelligence: a survey // Biomed. Eng. Online. – 2018. – № 17 (1). ‒ Р. 113.

35. Qin Z. Z., Sander M. S., Rai B. et al. Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems // Sci Rep 9. – 2019. – 15000.

36. Rahimzadeh M., Attar A. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2 // Inform. Med. Unlocked. – 2020. – № 19. ‒ Р. 100360.

37. Rahman M. T., Codlin A. J., Rahman M. M. et al. An evaluation of automated chest radiography reading software for tuberculosis screening among public- and private-sector patients // Eur. Respir. J. – 2017. – № 49 (5). ‒ Р. 1602159.

38. Rajpurkar P., Irvin J., Zhu K., Yang B., Mehta H., Duan T., Ding D., Bagul A., Langlotz C., Shpanskaya K. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning // ArXiv Prepr ArXiv:171105225 (2017).

39. Raoof S., Feigin D., Sung A., Raoof S., Irugulpati L., Rosenow E. C. 3rd. Interpretation of plain chest roentgenogram // Chest. – 2012. – № 141 (2). – P. 545-558.

40. Sim Y., Chung M. J., Kotter E. et al. Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs // Radiology. – 2020. – № 294 (1). – P. 199-209.

41. Taylor A. G., Mielke C., Mongan J. Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study // PLoS Med. – 2018. – № 15 (11). – Р. 1002697.

42. White C. S., Flukinger T., Jeudy J., Chen J. J. Use of a computer-aided detection system to detect missed lung cancer at chest radiography // Radiology. – 2009. – № 252 (1). – P. 273-281.

43. Xu T., Cheng I., Long R. et al. Novel coarse-to-fine dual scale technique for tuberculosis cavity detection in chest radiographs // J. Image Video Proc. – 2013. – 3.

44. Zaidi S. M. A., Habib S. S., Van Ginneken B. et al. Evaluation of the diagnostic accuracy of Computer-Aided Detection of tuberculosis on Chest radiography among private sector patients in Pakistan // Sci. Rep. – 2018. – № 8 (1). ‒ Р. 12339.

45. Zhou S., Zhang X., Zhang R. Identifying Cardiomegaly in ChestX-ray8 Using Transfer Learning // Stud Health Technol Inform. – 2019. – № 264. – P. 482‐486.