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Keras Slim ResNet-Based Prediction of Furcation Management Recommendations Among Dentists
Pradeep Kumar Yadalam1, Deepavalli Arumuganainar1, Seyed Ali Mosaddad2
1Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India, saveetha.com.
Purpose:
Furcation involvement occurs when periodontal deterioration reaches the roots of a multirooted tooth, making diagnosis and treatment difficult. Furcation management has shifted from periodontal care to unnecessary extraction and prosthetic replacement with or without implants. Prediction modeling with cutting-edge machine learning algorithms was used to select treatments from questionnaire responses. We used Keras ResNet to forecast dentists' furcation management recommendations.
Materials And Methods:
The participants were dentists with undergraduate or postgraduate degrees in fields other than periodontics and 5 years of practical experience. The study comprised 437 South Indian dentists aged 28-60 years. The author estimated the sample size based on previous research. We compared findings from a data robot tool employing a state-of-the-art model with Keras Slim and Light Gradient Boosting. Data were split 80/20 between training and testing.
Results:
Keras ResNet and light gradient improved accuracy by 84% in predicting the target class of furcation-related tooth treatment suggestions.
Conclusion:
The Keras Slim ResNet-based questionnaire-prediction model among dentists has demonstrated good accuracy, helping predict referral patterns for managing furcation-involved teeth. In addition, it encourages general dentists to refer complex furcation cases to periodontists for expert care consistently.
