Assessment of YOLOv3 for caries detection in bitewing radiographs based on the ICCMS™ radiographic scoring system.

Wannakamon Panyarak, Wattanapong Suttapak, Kittichai Wantanajittikul, Arnon Charuakkra, Sangsom Prapayasatok

April 2023 Clin Oral Investig

Synopsis of Social media discussions

The discussions reflect a generally positive view with mentions of YOLOv3's performance, such as its success in identifying advanced caries stages, while also noting limitations in early detection. Words like 'promising,' 'feasible,' and phrases emphasizing potential or room for improvement indicate a cautious optimism that aligns with the moderate scores assigned.

A
Agreement
 Moderate agreement

Most discussions acknowledge the study’s promising results and potential usefulness in dental diagnostics.

I
Interest
 Moderate level of interest

Participants show moderate curiosity, interested in how AI models like YOLOv3 can assist in radiograph analysis.

E
Engagement
 Moderate level of engagement

Comments include some references to the methodology and implications, but lack deep technical debate.

I
Impact
 Neutral impact

Overall, the discussions recognize the potential influence of the research but do not suggest immediate transformative change.

Social Mentions

YouTube

2 Videos

Twitter

3 Posts

News

1 Articles

Metrics

Video Views

114

Total Likes

3

Extended Reach

147

Social Features

6

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Dental Caries Detection Using YOLOv3 on Bitewing Radiographs

Dental Caries Detection Using YOLOv3 on Bitewing Radiographs

We assessed the feasibility of YOLOv3 to detect and classify dental caries on bitewing radiographs using the ICCMS radiographic scoring system. The model showed promising results, especially for advanced lesions, but faced challenges with early enamel caries detection.

DentalAI

November 19, 2025

104 views

Evaluating YOLOv3 for Dental Caries Detection on Bitewing Radiographs

Evaluating YOLOv3 for Dental Caries Detection on Bitewing Radiographs

We assessed the feasibility of YOLOv3 to detect and classify dental caries on bitewing radiographs using the ICCMS radiographic scoring system under two IoU thresholds. The model performed well in identifying advanced caries stages, with challenges in early enamel lesions.

DentalAI

November 19, 2025

11 views

  • Nielsen Pereira
    @nielsantper (Twitter)

    RT @Dentalai_pt: This study assessed the feasibility of YOLOv3 to detect and classify dental caries on bitewing radiographs using the ICCMS…
    view full post

    November 21, 2025

    1

  • Dental AI
    @Dentalai_pt (Twitter)

    DOI https://t.co/HMOfC7r0JE
    view full post

    November 21, 2025

  • Dental AI
    @Dentalai_pt (Twitter)

    This study assessed the feasibility of YOLOv3 to detect and classify dental caries on bitewing radiographs using the ICCMS™ radiographic scoring system. Watch on YT: https://t.co/PUtNixEyrr
    view full post

    November 21, 2025

    1

    1

  • Automatic Caries Detection in Bitewing Radiographs Part I—Deep ...

    https://doi.org/10.1007/s00784-022-04801-6 using a deep convolutional neural network-based software. Caries 28. Panyarak W, Wantanajittikul K, Suttapak W et ...
    view full post

    December 21, 2025

    News

Abstract Synopsis

  • The study evaluated the effectiveness of the YOLOv3 model in detecting and classifying dental caries in bitewing radiographs using the ICCMS scoring system, focusing on different IoU thresholds (0.5 and 0.75).
  • YOLOv3 performed well in identifying certain caries stages, especially more advanced lesions like those affecting the dentin and pulp, but was less accurate in predicting initial enamel caries.
  • Overall, YOLOv3 showed promising results for assisting dentists in caries detection and classification, though it struggled with early-stage caries, indicating room for improvement in sensitivity for less severe cases.]