Literature DB >> 34686815

The ADEPT study: a comparative study of dentists' ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of AssistDent artificial intelligence software.

Hugh Devlin1, Tomos Williams2, Jim Graham3, Martin Ashley4.   

Abstract

Introduction Reversal of enamel-only proximal caries by non-invasive treatments is important in preventive dentistry. However, detecting such caries using bitewing radiography is difficult and the subtle patterns are often missed by dental practitioners.Aims To investigate whether the ability of dentists to detect enamel-only proximal caries is enhanced by the use of AssistDent artificial intelligence (AI) software.Materials and methods In the ADEPT (AssistDent Enamel-only Proximal caries assessmenT) study, 23 dentists were randomly divided into a control arm, without AI assistance, and an experimental arm, in which AI assistance provided on-screen prompts indicating potential enamel-only proximal caries. All participants analysed a set of 24 bitewings in which an expert panel had previously identified 65 enamel-only carious lesions and 241 healthy proximal surfaces.Results The control group found 44.3% of the caries, whereas the experimental group found 75.8%. The experimental group incorrectly identified caries in 14.6% of the healthy surfaces compared to 3.7% in the control group. The increase in sensitivity of 71% and decrease in specificity of 11% are statistically significant (p <0.01).Conclusions AssistDent AI software significantly improves dentists' ability to detect enamel-only proximal caries and could be considered as a tool to support preventive dentistry in general practice.
© 2021. The Author(s).

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Year:  2021        PMID: 34686815      PMCID: PMC8536492          DOI: 10.1038/s41415-021-3526-6

Source DB:  PubMed          Journal:  Br Dent J        ISSN: 0007-0610            Impact factor:   2.727


  17 in total

1.  Comparison of diagnostic yields of clinical and radiographic caries examinations in children of different age.

Authors:  V Machiulskiene; B Nyvad; V Baelum
Journal:  Eur J Paediatr Dent       Date:  2004-09       Impact factor: 2.231

2.  Cariogram--a multifactorial risk assessment model for a multifactorial disease.

Authors:  Douglas Bratthall; Gunnel Hänsel Petersson
Journal:  Community Dent Oral Epidemiol       Date:  2005-08       Impact factor: 3.383

3.  Variables affecting the inter- and intra-examiner reliability of ICDAS for occlusal caries diagnosis in permanent molars.

Authors:  Muawia A Qudeimat; Qasem D Alomari; Yacoub Altarakemah; Nour Alshawaf; Eino J Honkala
Journal:  J Public Health Dent       Date:  2015-06-10       Impact factor: 1.821

4.  Accuracy at radiography and probing for the diagnosis of proximal caries.

Authors:  I Mejàre; H G Gröndahl; K Carlstedt; A C Grever; E Ottosson
Journal:  Scand J Dent Res       Date:  1985-04

Review 5.  Convolutional neural networks for dental image diagnostics: A scoping review.

Authors:  Falk Schwendicke; Tatiana Golla; Martin Dreher; Joachim Krois
Journal:  J Dent       Date:  2019-11-05       Impact factor: 4.379

6.  Risk factors associated with new caries lesions in permanent first molars in children: a 5-year historical cohort follow-up study.

Authors:  Carmen Llena; Elena Calabuig
Journal:  Clin Oral Investig       Date:  2017-10-23       Impact factor: 3.573

7.  A comparison of clinical and radiographic caries diagnoses in posterior teeth of 12-year-old Lithuanian children.

Authors:  V Machiulskiene; B Nyvad; V Baelum
Journal:  Caries Res       Date:  1999 Sep-Oct       Impact factor: 4.056

Review 8.  Assessing caries risk--using the Cariogram model.

Authors:  Gunnel Hänsel Petersson
Journal:  Swed Dent J Suppl       Date:  2003

9.  Artificial Intelligence in Dentistry: Chances and Challenges.

Authors:  F Schwendicke; W Samek; J Krois
Journal:  J Dent Res       Date:  2020-04-21       Impact factor: 6.116

10.  The impact of ICDAS on occlusal caries treatment recommendations for high caries risk patients: an in vitro study.

Authors:  Muawia A Qudeimat; Yacoub Altarakemah; Qasem Alomari; Nour Alshawaf; Eino Honkala
Journal:  BMC Oral Health       Date:  2019-03-07       Impact factor: 2.757

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  3 in total

Review 1.  Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology.

Authors:  Kuo Feng Hung; Qi Yong H Ai; Yiu Yan Leung; Andy Wai Kan Yeung
Journal:  Clin Oral Investig       Date:  2022-04-19       Impact factor: 3.606

Review 2.  Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)-A Systematic Review.

Authors:  Sanjeev B Khanagar; Khalid Alfouzan; Mohammed Awawdeh; Lubna Alkadi; Farraj Albalawi; Abdulmohsen Alfadley
Journal:  Diagnostics (Basel)       Date:  2022-04-26

Review 3.  Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis.

Authors:  Andrej Thurzo; Wanda Urbanová; Bohuslav Novák; Ladislav Czako; Tomáš Siebert; Peter Stano; Simona Mareková; Georgia Fountoulaki; Helena Kosnáčová; Ivan Varga
Journal:  Healthcare (Basel)       Date:  2022-07-08
  3 in total

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