AI-assisted versus traditional radiographic interpretation for early caries detection.

Vikram Karande, Mohd Zeeshan Ahmad, Vishal Kulkarni, Priyanka Chandra, Sowjanya Gunukula, Mousami Kundu, Ritik Kashwani

Abstract read

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Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Vikram KarandeDepartment of Dentistry, AIIMS, Rajkot, Gujarat, India.

Mohd Zeeshan AhmadDepartment of Conservative Dentistry & Endodontics, Rama Dental College, Hospital & Research Centre, Kanpur, Uttar Pradesh, India.

Vishal KulkarniDepartment of Oral and Maxillofacial Surgery, Army Dental Centre Research and Referral, Delhi Cantt, India.

Priyanka ChandraDepartment of Conservative Dentistry and Endodontics, Kothiwal Dental college and Research Centre Kanth Road, Moradabad, Uttar Pradesh, India.

Sowjanya GunukulaPrivate Partition, Flomo Dental Private Clinic 295 Ukiah St, Lewisville, Texas, USA.

Mousami KunduDepartment of Oral & Maxillofacial Surgery, Awadh Dental College and Hospital, NH-33, Dalma, Bhilaipahari, Dalma, Jamshedpur, Jharkhand, India.

Ritik KashwaniDepartment of Oral Medicine and Radiology, School of Dental Sciences, Sharda University, Greater Noida, Uttar Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection of dental caries is critical for implementing effective preventive measures, yet traditional radiographic interpretation often lacks consistency due to observer variability. Therefore, it is of interest to compare the diagnostic performance of AI-assisted and conventional radiographic interpretations for detecting early carious lesions. Hence, a total of 100 subjects were randomly assigned to either the AI-assisted or conventional interpretation group, with diagnostic accuracy assessed using sensitivity, specificity and overall accuracy. Thus, we show that AI-assisted interpretation outperformed conventional methods, with higher sensitivity (84%) and diagnostic accuracy (82%). This advancement highlights AI's potential to significantly improve early caries detection in dental practice.

Indexed as

Artificial intelligence (AI)dental cariesdiagnostic accuracyearly detectionradiographic interpretation

Identifiers

PMID42282288

DOI10.6026/973206300222278

PMCPMC13252351

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.