ArticleThe Angle orthodontist2020
How much deep learning is enough for automatic identification to be reliable?
Article in The Angle orthodontist, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis.Journal of digital imaging · 2023Pooled it
- Automated localization of mandibular landmarks in the construction of mandibular median sagittal plane.European journal of medical research · 2024Trial
- Comparative diagnostic accuracy of a You Only Look Once-11s deep learning model and human observers for detection of periapical lesions on intraoral periapical radiographs.Journal of conservative dentistry and endodontics · 2026Article
- Agreement of WebCeph-Based Automated and Expert-Adjusted Cephalometric Analyses with Manual and Dolphin Tracings.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning for Angle classification based on intraoral photographs: an interpretability perspective.BMC oral health · 2026Article
- Artificial neural network in orthodontic therapeutic extractions.Bioinformation · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Factors influencing the predictive performance of artificial intelligence for craniofacial growth.The Angle orthodontist · 2025Article
- What amount of data is required to develop artificial intelligence that can accurately predict soft tissue changes after orthognathic surgery?The Angle orthodontist · 2025Article
- Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection.The Angle orthodontist · 2025Article
- Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children.The Angle orthodontist · 2025Article
- Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm.Scientific reports · 2024Article
- Factors influencing the development of artificial intelligence in orthodontics.Orthodontics & craniofacial research · 2024Article
- Evaluation of automated photograph-cephalogram image integration using artificial intelligence models.The Angle orthodontist · 2024Article
- Accuracy of web-based automated versus digital manual cephalometric landmark identification.Clinical oral investigations · 2024Article
- Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.The Angle orthodontist · 2024Article
- Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?The Angle orthodontist · 2024Article
- Review
- Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.The Angle orthodontist · 2024Article
- Accuracy and tracing time of cephalometric analyses on a tablet or desktop computer : A prospective study.Head & face medicine · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo determine the optimal quantity of learning data needed to develop artificial intelligence (AI) that can automatically identify cephalometric landmarks. MATERIALS AND
methodsA total of 2400 cephalograms were collected, and 80 landmarks were manually identified by a human examiner. Of these, 2200 images were chosen as the learning data to train AI. The remaining 200 images were used as the test data. A total of 24 combinations of the quantity of learning data (50, 100, 200, 400, 800, 1600, and 2000) were selected by the random sampling method without replacement, and the number of detecting targets per image (19, 40, and 80) were used in the AI training procedures. The training procedures were repeated four times. A total of 96 different AIs were produced. The accuracy of each AI was evaluated in terms of radial error.
resultsThe accuracy of AI increased linearly with the increasing number of learning data sets on a logarithmic scale. It decreased with increasing numbers of detection targets. To estimate the optimal quantity of learning data, a prediction model was built. At least 2300 sets of learning data appeared to be necessary to develop AI as accurate as human examiners.
conclusionsA considerably large quantity of learning data was necessary to develop accurate AI. The present study might provide a basis to determine how much learning data would be necessary in developing AI.
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