Title

Development and validation of an AI model predicting post-extraction complications: A multicenter cohort study protocol

Authors

Soumya Ranjan Patra¹*, Shweta Hegde¹, Neeraj Chandra², Shruti Bhatnagar³, Moon Roy⁴, Priya Amrit⁵ & Aakash Kohli⁵

Affiliation

¹Department of Oral Medicine & Radiology, Triveni Institute of Dental Sciences, Hospital & Research Centre (TIDSHRC), Bilaspur, Chhattisgarh, India; ²Department of Periodontics, Institute of Dental sciences, Bareilly, Uttar Pradesh, India; ³Department of Periodontics, Chhattisgarh dental college and research Institute, Rajnandgaon, Chhattisgarh, India; ⁴Department of Pediatric & Preventive Dentistry, Triveni Institute of Dental Sciences, Hospital & Research Centre (TIDSHRC), Bilaspur, Chhattisgarh, India; ⁵Department of Dentistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India; *Corresponding author

Email

Soumya Ranjan Patra - E-mail: hello@patradental.in; Phone: +91 90781 83678
Shweta Hegde - E-mail: drshwetashetty@gmail.com; Phone: +91 83191 76081
Neeraj Chandra - E-mail: dr.neeraj.chandra05@gmail.com; Phone: +91 96277 49449
Shruti Bhatnagar - E-mail: bhatnagarshruti9@gmail.com; phone: +91 74158 32733
Moon Roy - E-mail: hello.drmoonroy@gmail.com; Phone: +91 70052 77735
Priya Amrit - E-mail: amrit20priya@gmail.com; Phone: +91 80852 49650
Aakash Kohli - E-mail: aakashkohli22@gmail.com

Article Type

Research Article

Date

Received September 1, 2026; Revised September 30, 2026; Accepted September 30, 2026, Published September 30, 2026

Abstract

Dry socket (alveolar osteitis) and post-operative surgical-site infection (SSI) are most common complications affecting 1-20% extraction sites. Risk factors include smoking, diabetes, prolonged surgery and operative trauma. A multicenter cohort using retrospective derivation (n≈2000, 3–5 centers) and prospective validation (12–18 months, ≥2 centers) will be done. The patients included will be adults ≥18 years undergoing extraction and outcome assessed will be dry socket (pain, exposed bone days 1–5); SSI (purulence/swelling within 30 days). The expected outcome model should be able to predict the prognosis of the dry socket and SSI, thus reducing the injudicious use of antibiotics and increase in expertise of the surgeon.

Keywords

Dry socket, surgical site infection, machine learning, tooth extraction, comorbidities

Citation

Patra et al. Bioinformation 22(9): 5861-5865 (2026)

Edited by

Ritik Kashwani

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.