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



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.


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


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.


Artificial intelligence Case difficulty Machine learning Referral Treatment planning 



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.


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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

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