|Year : 2018 | Volume
| Issue : 1 | Page : 1-2
Artificial intelligence in dentomaxillofacial radiology: Hype or future?
Independent Unit of Propaedeutics of Dentomaxillofacial Radiology, Medical University of Lublin, Lublin, Poland
|Date of Web Publication||26-Apr-2018|
Independent Unit of Propaedeutics of Dentomaxillofacial Radiology, Medical University of Lublin, Lublin
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Rozylo-Kalinowska I. Artificial intelligence in dentomaxillofacial radiology: Hype or future?. J Oral Maxillofac Radiol 2018;6:1-2
|How to cite this URL:|
Rozylo-Kalinowska I. Artificial intelligence in dentomaxillofacial radiology: Hype or future?. J Oral Maxillofac Radiol [serial online] 2018 [cited 2021 Mar 7];6:1-2. Available from: https://www.joomr.org/text.asp?2018/6/1/1/231359
From the onset of radiology in the end of the 19th century, this discipline has always been considered related to visual skills and image reporting has been subjective. The famous sentence “the eye sees only what the mind is prepared to comprehend” (by some attributed to the French philosopher Henri Bergson and by others to a Canadian novelist Robertson Davies) is the best description of radiology. As adepts, we discover fewer findings in radiographs or cross-sectional imaging studies not only because our eyes are not attentive enough but also due to the fact that our knowledge is still inadequate and experience meager. We may overlook some signs and symptoms when we are not aware that they may occur and do not systematically look for them in radiographs. By and by we gain experience and our reports become more and more sophisticated. However, with time passing by routine takes over, fatigue, aging, and even professional burn out induced by an increasing workload may again negatively influence our quality of reporting. Even if our enthusiasm for work is not worn out by daily labor, increase in numbers of newly qualified radiologists does not go hand in hand with rising numbers of panoramics and cone beam computed tomography scans performed day in day out all over the world. This hiatus is even more striking in dentomaxillofacial radiology as the specialists are sparse and radiographic machines installed in dental offices prolific. One of the solutions would be to train more specialists but still there are many countries, in which dentomaxillofacial radiology is not recognized as a separate specialty and no specialists are trained at all apart from some enthusiastic hotheads dedicated to this discipline. Can the so-called “artificial intelligence” (AI) – or rather to be precise – machine learning as its core component-be the solution? Some consider it a threat to humans and others treat it as a kind of enhancement of our skills. Can “intelligent” machines replace radiologists and put them out of business as AI algorithm are operational 24/7 at lower costs than humans? Maybe the machines can simply aid radiologists in their daily routines?, For example, to relieve them from the burden of tiresome, reporting of impacted canines thus generating more time for radiologists to focus on more difficult cases and/or interventional procedures. Can deep learning machines improve our efficiency and accuracy of diagnostics resulting in a better patient outcome? Will AI change the landscape of clinical practice and scientific research? Or simply create faster turnaround times and result in a better quality of life for radiologists? When writing this editorial I could retrieve only one pilot study directly related to dentomaxillofacial radiology and dealing with an automated technique used to stage lower third molar development on panoramic radiographs for age estimation. The easiest fields for the evaluation of efficiency of application of AI are diagnostics of lung nodules and breast cancer screening, so no wonder that papers and websites dealing with these issues are numerous., However, they are by far outnumbered by sensational debates on pros and cons of artificial intelligence. So, is machine learning just a hype or the brightest future of dentomaxillofacial radiology? Let's wait and see!
| References|| |
De Tobel J, Radesh P, Vandermeulen D, Thevissen PW. An automated technique to stage lower third molar development on panoramic radiographs for age estimation: A pilot study. J Forensic Odontostomatol 2017;2:42-54.
Dreyer K, Allen B. Artificial intelligence in health care: Brave new world or golden opportunity? J Am Coll Radiol 2018. pii: S1546-1440(18)30032-2.
Mayo RC, Leung J. Artificial intelligence and deep learning – Radiology's next frontier? Clin Imaging 2017;49:87-8.
Miller DD, Brown EW. Artificial intelligence in medical practice: The question to the answer? Am J Med 2018;131:129-33.
Syeda-Mahmood T. Role of big data and machine learning in diagnostic decision support in radiology. J Am Coll Radiol 2018;15:569-76.
Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, et al.
Artificial intelligence and machine learning in radiology: Opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 2018;15:504-8.
Xing L, Krupinski EA, Cai J. Artificial intelligence will soon change the landscape of medical physics research and practice. Med Phys 2018.