ArticleBMC medical imaging2022
Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
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- TensorFlow-based MobileNetV2 U-Net tumor segmentation and multiparametric MRI radiomics for predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma.Therapeutic advances in medical oncology · 2026Article
- Artificial intelligence model for application in dental traumatology.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2025Article
- Advances in Periodontal Diagnostics: Application of MultiModal Language Models in Visual Interpretation of Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Identifying ovarian cancer with machine learning DNA methylation pattern analysis.Scientific reports · 2025Article
- Different artificial neural networks for predicting burnout risk in Italian anesthesiologists.Journal of anesthesia, analgesia and critical care · 2025Article
- Dental age estimation using a convolutional neural network algorithm on panoramic radiographs: A pilot study in Indonesia.Imaging science in dentistry · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs.AJNR. American journal of neuroradiology · 2024Article
- Review
- Fully automated deep learning approach to dental development assessment in panoramic radiographs.BMC oral health · 2024Article
- Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification.BMC oral health · 2024Article
- Computational methods in glaucoma research: Current status and future outlook.Molecular aspects of medicine · 2023Review
- An explainable artificial intelligence system for diagnosingTherapeutic advances in gastroenterology · 2023Article
- Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.International journal of molecular sciences · 2022Article
- Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes.International journal of molecular sciences · 2022Article
- Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer.Cancers · 2022Article
- Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review.Frontiers in artificial intelligence · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to propose the combinations of image processing and machine learning model to segment the maturity development of the mandibular premolars using a Keras-based deep learning convolutional neural networks (DCNN) model.
methodsA dataset consisting of 240 images (20 images per stage per sex) of retrospect digital dental panoramic imaging of patients between 5 and 14 years of age was retrieved. In image preprocessing, abounding box with a dimension of 250 × 250 pixels was assigned to the left mandibular first (P1) and second (P2) permanent premolars. The implementation of dynamic programming of active contour (DP-AC) and convolutions neural network on images that require the procedure of image filtration using Python TensorFlow and Keras libraries were performed in image segmentation and classification, respectively.
resultsImage segmentation using the DP-AC algorithm enhanced the visibility of the image features in the region of interest while suppressing the image's background noise. The proposed model has an accuracy of 97.74%, 96.63% and 78.13% on the training, validation, and testing set, respectively. In addition, moderate agreement (Kappa value = 0.58) between human observer and computer were identified. Nonetheless, a robust DCNN model was achieved as there is no sign of the model's over-or under-fitting upon the learning process.
conclusionsThe application of digital imaging and deep learning techniques used by the DP-AC and convolutions neural network algorithms to segment and identify premolars provides promising results for semi-automated forensic dental staging in the future.
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