Advertisement

The precision of case difficulty and referral decisions: an innovative automated approach

  • Shivani MallisheryEmail author
  • Pavan Chhatpar
  • K. S. Banga
  • Trusha Shah
  • Pankaj Gupta
Original Article
  • 7 Downloads

Abstract

Objectives

Endodontic treatment works as a successful treatment modality in several cases. However, it may fail due to some reasons unforeseeable by the dentist. Many failures can be prevented by carefully assessing the difficulty level of the case before initiating treatment or by referral to a specialist. This study presents an approach using machine learning to generate an algorithm which can help predict the difficulty level of the case and decide about a referral, with the help of the standard American Association of Endodontists (AAE) Endodontic Case Difficulty Assessment Form.

Materials and methods

Using the AAE Endodontic Case Difficulty Form after obtaining the patients’ consent, 500 potential root canal patients were diagnosed. The filled forms were assessed by two pre-calibrated endodontists, and, in cases of conflicting opinion, a third endodontist’s opinion was taken. Artificial neural network was used for generating the algorithm.

Results

Using 500 filled AAE forms, a sensitivity of 94.96% was achieved by the machine learning algorithm.

Conclusion

This study provides an option for automation to the conventional method of predicting the difficulty level of a case, thus increasing the speed of decision-making and referrals if necessary.

Clinical relevance

An AAE Endodontic Case Difficulty Assessment Form when utilized along with machine learning can assist general dentists in rapid assessment of the case difficulty. This is a helpful tool in developing countries, where endodontic treatment and referral guidelines are often neglected. It also helps to make difficulty level assessments easier for novice practitioners, when they are in doubt about the same.

Keywords

Artificial intelligence Case difficulty Machine learning Referral Treatment planning 

Notes

Acknowledgments

We thank all the participants of this study.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study has been approved by the appropriate Ethics Committee (Nair Hospital Dental College; Mumbai, India approval no. EC-84/CONS-09ND/2018). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Reiser SJ, Reiser SJ. (1981) Medicine and the reign of technology. Cambridge University PressGoogle Scholar
  2. 2.
    Foster KR, Koprowski R, Skufca JD (2014) Machine learning, medical diagnosis, and biomedical engineering research-commentary. Biomed Eng 13(1):94.  https://doi.org/10.1186/1475-925X-13-94 Google Scholar
  3. 3.
    Carrotte P (2004) Endodontics: part 2 diagnosis and treatment planning. Br Dent J 197(5):231.  https://doi.org/10.1038/sj.bdj.4811612 CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2:230–243.  https://doi.org/10.1136/svn-2017-000101 CrossRefGoogle Scholar
  6. 6.
    Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. ElsevierGoogle Scholar
  7. 7.
    Kim EY, Lim KO, Rhee HS (2009) Predictive modeling of dental pain using neural network. Stud Health Technol Inform 146:745–746Google Scholar
  8. 8.
    Käkilehto T, Salo S, Larmas M (2009) Data mining of clinical oral health documents for analysis of the longevity of different restorative materials in Finland. Int J Med Inform 78(12):e68–e74.  https://doi.org/10.1016/j.ijmedinf.2009.04.004 CrossRefGoogle Scholar
  9. 9.
    Korhonen M, Gundagar M, Suni J, Salo S, Larmas M (2009) A practice-based study of the variation of diagnostics of dental caries in new and old patients of different ages. Caries Res 43(5):339–344.  https://doi.org/10.1159/000231570 CrossRefGoogle Scholar
  10. 10.
    https://firebase.google.com/. Assessed on 25/09/2018
  11. 11.
    Deng N, Tian Y, Zhang C (2012) Support vector machines: optimization based theory, algorithms, and extensions. Chapman and Hall/CRCGoogle Scholar
  12. 12.
    Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning. MIT press, CambridgeGoogle Scholar
  13. 13.
    Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159Google Scholar
  14. 14.
    Swartz DB, Skidmore AE, Griffin JA (1983) Twenty years of endodontic success and failure. J Endod 9(5):198–202.  https://doi.org/10.1016/S0099-2399(83)80092-2 CrossRefGoogle Scholar
  15. 15.
    Chandra BS, Gopikrishna V (2014) ENDODONTIC PRACTICE. Wolters Kluwer IndiaGoogle Scholar
  16. 16.
  17. 17.
    Ingle JI, Bakland LK, Baumgartner JC (eds) (2008). Ingle's endodontics 6. PMPH USAGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Nair Hospital Dental CollegeMumbaiIndia
  2. 2.Northeastern UniversityBostonUSA
  3. 3.Department of Conservative Dentistry and Endodontics, Nair Hospital Dental CollegeMumbaiIndia

Personalised recommendations