Clinical validation of a web-based decision support system for periodontal diagnosis, risk assessment, and recall planning: A multicenter study

Purpose Application of the 2017 American Academy of Periodontology/European Federation of Periodontology (AAP/EFP) classification requires integration of multiple clinical, radiographic, and risk-related parameters, which may be challenging and time-consuming in routine clinical practice. Digital decision-support systems may assist clinicians by improving diagnostic consistency and workflow efficiency. Therefore, the aim of the current study is to evaluate the diagnostic agreement, accuracy, and clinical efficiency of a web-based automated decision-support system for periodontal diagnosis, risk assessment, and recall planning in comparison with gold-standard clinician assessment. Methods This two-phase study included Rule-Based Algorithm Development and Verification using 2000 anonymized periodontal records and prospective clinical validation in 328 consecutive patients. Automated outputs for periodontal staging, grading, periodontal risk assessment, and supportive periodontal therapy recall recommendations were compared with independent clinician diagnosis. Diagnostic agreement was assessed using Cohen's kappa statistics, while sensitivity, specificity, predictive values, and diagnostic time were also analyzed. Results The automated system demonstrated moderate agreement with clinician diagnosis for periodontal staging and grading, with higher levels of agreement observed for periodontal risk categorization and recall interval recommendations. Sensitivity and negative predictive value for identification of advanced periodontitis (Stage III–IV) were high, while most diagnostic discrepancies occurred between adjacent diagnostic categories. Use of the system resulted in a significant reduction in diagnostic time compared with conventional clinical assessment (p < 0.0001). Conclusions The web-based decision-support system showed clinically acceptable agreement with clinician diagnosis and improved diagnostic efficiency. It may be used as an adjunct to support standardized periodontal diagnosis, risk assessment, and recall planning, while comprehensive clinical examination and professional judgment remain essential.

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Publication Details

Journal
Journal of Oral Biology and Craniofacial Research
Published
2026-08-25
DOI
https://doi.org/10.1016/j.jobcr.2026.101525
Primary Topic
Oral microbiology and periodontitis research
Type
article
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article

Clinical validation of a web-based decision support system for periodontal diagnosis, risk assessment, and recall planning: A multicenter study

Aruna Nautiyal, Sonali Singh, Sakshi Singh, Tanya Attre et al.
Journal of Oral Biology and Craniofacial Research
Oral microbiology and periodontitis research
article

Clinical validation of a web-based decision support system for periodontal diagnosis, risk assessment, and recall planning: A multicenter study

Aruna Nautiyal, Sonali Singh, Sakshi Singh, Tanya Attre, kumar Saurav, Shilpi Tyagi
article en

Abstract

Purpose Application of the 2017 American Academy of Periodontology/European Federation of Periodontology (AAP/EFP) classification requires integration of multiple clinical, radiographic, and risk-related parameters, which may be challenging and time-consuming in routine clinical practice. Digital decision-support systems may assist clinicians by improving diagnostic consistency and workflow efficiency. Therefore, the aim of the current study is to evaluate the diagnostic agreement, accuracy, and clinical efficiency of a web-based automated decision-support system for periodontal diagnosis, risk assessment, and recall planning in comparison with gold-standard clinician assessment. Methods This two-phase study included Rule-Based Algorithm Development and Verification using 2000 anonymized periodontal records and prospective clinical validation in 328 consecutive patients. Automated outputs for periodontal staging, grading, periodontal risk assessment, and supportive periodontal therapy recall recommendations were compared with independent clinician diagnosis. Diagnostic agreement was assessed using Cohen's kappa statistics, while sensitivity, specificity, predictive values, and diagnostic time were also analyzed. Results The automated system demonstrated moderate agreement with clinician diagnosis for periodontal staging and grading, with higher levels of agreement observed for periodontal risk categorization and recall interval recommendations. Sensitivity and negative predictive value for identification of advanced periodontitis (Stage III–IV) were high, while most diagnostic discrepancies occurred between adjacent diagnostic categories. Use of the system resulted in a significant reduction in diagnostic time compared with conventional clinical assessment (p < 0.0001). Conclusions The web-based decision-support system showed clinically acceptable agreement with clinician diagnosis and improved diagnostic efficiency. It may be used as an adjunct to support standardized periodontal diagnosis, risk assessment, and recall planning, while comprehensive clinical examination and professional judgment remain essential.

Journal of Oral Biology and Craniofacial ResearchVol. 16(5)
Santosh University (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 9%
Oral microbiology and periodontitis research
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