Keywords :
Automated diagnosis, Artificial intelligence in healthcare, Deep learning, Machine vision, Rhinological imaging, Rhinology
Citation Information :
Kumar S, Dutta A, Biradar K. Integrating Machine Vision in Rhinology: The Advent of Automated Diagnostic Systems and their Clinical Implications. Int J Otorhinolaryngol Clin 2024; 16 (3):170-174.
Objective: The purpose of this review is to provide a comprehensive analysis of the potential transformational impact of machine vision in the field of rhinology, specifically examining its implications for both diagnosis and treatment.
Methodology: A thorough review of the literature was performed on databases, which include PubMed, Google Scholar, Medline, and DOAJ. By employing a combination of keywords and MeSH phrases, we were able to identify academic articles that reveal an association between the areas of rhinology and machine vision. Out of an initial selection of 42 publications, a total of 30 were carefully selected using rigorous inclusion and exclusion criteria.
Results: The integration of machine vision technology in the rhinology field provides a major improvement in diagnosis accuracy and the development of treatment strategies. The review emphasized the importance of supervised learning, especially deep learning methods, in the analysis and interpretation of rhinological images. However, there remain persistent issues that primarily result from the limited accessibility of thoroughly annotated datasets.
Conclusion: The application of machine vision technology presents an opportunity for improving the accuracy of rhinological diagnoses and treatment approaches. The success of this endeavor depends on the building of synergistic relationships between professionals in artificial intelligence (AI) and clinical practitioners, along with the collection of datasets of superior quality. The integration of human capabilities and technological advancement is anticipated to create a transformative effect on patient care in the field of rhinology as it keeps on evolving.
Hildenbrand T, Weber RK, Brehmer D. Rhinology and facial plastic surgery. Springer Science & Business Media. 2009.
Fokkens WJ, Lund VJ, Mullol J, et al. EPOS 2012: European position paper on rhinosinusitis and nasal polyps. Rhinology 2012;50(1):1–12. DOI: 10.4193/Rhino12.000.
Kamburoğlu K, Schulze D, Murat S, et al. CBCT imaging of sinonasal disease. Maxillofacial Cone Beam Computed Tomography: Principles, Techniques and Clinical Applications 2018:1155–1205. DOI: 10.1007/978-3-319-62061-9_30.
Hopkins C, Browne JP, Slack R, et al. The Lund-Mackay staging system for chronic rhinosinusitis: How is it used and what does it predict? Otolaryngol Head Neck Surg 2007;137(4):155–161. DOI: 10.1016/j.otohns.2007.02.004.
Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med 2019;25:44–56. DOI: 10.1038/s41591-018-0300-7.
Gray H. Anatomy of the human body. Lea & Febiger; 1918.
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Ann Rev of Biomed Eng 2017;19:221–248. DOI: 10.1146/annurev-bioeng-071516-044442.
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Medical Image Analysis 2017;42:60–88. DOI: 10.1016/j.media.2017.07.005.
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316(22):2402–2410. DOI: 10.1001/jama.2016.17216.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542(7639):115–118. DOI: 10.1038/nature21056.
Rajpurkar P. CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. ArXiv abs/1711 2017;5225. DOI: 10.48550/arXiv.1711.05225.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–444. DOI: 10.1038/nature14539.
Liu L, Ouyang W, Wang X, et al. Deep learning for generic object detection: A survey. International Journal of Computer Vision 2020;128:261–318. DOI: 10.1007/s11263-019-01247-4.
Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science 2015;349:255–260. DOI: 10.1126/science.aaa8415.
Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer 2018;18(8):500–510. DOI: 10.1038/s41568-018-0016-5.
Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: A retrospective study. Lancet 2018;392(10162):2388–2396. DOI: 10.1016/S0140-6736(18)31645-3.
Lakhani P, Sundaram B. Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017;284(2):574–582. DOI: 10.1148/radiol.2017162326.
Gibson E, Giganti F, Hu Y, et al. Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans Med Imaging. 2018;37(8):1822–1834. DOI: 10.1109/TMI.2018.2806309.
De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24(9):1342–1350. DOI: 10.1038/s41591-018-0107-6.
Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012;16(5):933–951. DOI: 10.1016/j.media.2012.02.005.
Jha S, Topol EJ. Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA 2016;316(22):2353–2354. DOI: 10.1001/jama.2016.17438.
Price WN 2nd, Cohen IG. Privacy in the age of medical big data. Nat Med 2019;25(1):37–43. DOI: 10.1038/s41591-018-0272-7.
Castelvecchi D. Can we open the black box of AI? Nature 2016;538:20–23. DOI: 10.1038/538020a.
Maier-Hein L, Vedula SS, Speidel S, et al. Surgical data science for next-generation interventions. Nat Biomed Eng 2017;1(9):691–696. DOI: 10.1038/s41551-017-0132-7.
Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: Review, opportunities, and challenges. Brief Bioinform 2018;19(6):1236–1246. DOI: 10.1093/bib/bbx044.
