Predicting the Risk of Dental Implant Loss Using Deep Learning

Application of artificial intelligence in dental implant prognosis: A scoping review

OBJECTIVES

The purpose of this scoping review was to evaluate the performance of artificial intelligence (AI) in the prognosis of dental implants.

DATA

Studies that analyzed the performance of AI models in the prediction of implant prognosis based on medical records or radiographic images. Quality assessment was conducted using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies.

SOURCES

This scoping review included studies published in English up to October 2023 in MEDLINE/PubMed, Embase, Cochrane Library, and Scopus. A manual search was also performed.

STUDY SELECTION

Of 892 studies, full-text analysis was conducted in 36 studies. Twelve studies met the inclusion criteria. Eight used deep learning models, 3 applied traditional machine learning algorithms, and 1 study combined both types. The performance was quantified using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic area under curves (ROC AUC). The prognostic accuracy was analyzed and ranged from 70 % to 96.13 %.

CONCLUSIONS

AI is a promising tool in evaluating implant prognosis, but further enhancements are required. Additional radiographic and clinical data are needed to improve AI performance in implant prognosis.

CLINICAL SIGNIFICANCE

AI can predict the prognosis of dental implants based on radiographic images or medical records. As a result, clinicians can receive predicted implant prognosis with the assistance of AI before implant placement and make informed decisions.

Deep learning for the identification of ridge deficiency around dental implants

OBJECTIVES

This study aimed to use a deep learning (DL) approach for the automatic identification of the ridge deficiency around dental implants based on an image slice from cone-beam computerized tomography (CBCT).

MATERIALS AND METHODS

Single slices crossing the central long-axis of 630 mandibular and 845 maxillary virtually placed implants (4-5 mm diameter, 10 mm length) in 412 patients were used. The ridges were classified based on the intraoral bone-implant support and sinus floor location. The slices were either preprocessed by alveolar ridge homogenizing prior to DL (preprocessed) or left unpreprocessed. A convolutional neural network with ResNet-50 architecture was employed for DL.

RESULTS

The model achieved an accuracy of >98.5% on the unpreprocessed image slices and was found to be superior to the accuracy observed on the preprocessed slices. On the mandible, model accuracy was 98.91 ± 1.45%, and F1 score, a measure of a model's accuracy in binary classification tasks, was lowest (97.30%) on the ridge with a combined horizontal-vertical defect. On the maxilla, model accuracy was 98.82 ± 1.11%, and the ridge presenting an implant collar-sinus floor distance of 5-10 mm with a dehiscence defect had the lowest F1 score (95.86%). To achieve >90% model accuracy, ≥441 mandibular slices or ≥592 maxillary slices were required.

CONCLUSIONS

The ridge deficiency around dental implants can be identified using DL from CBCT image slices without the need for preprocessed homogenization. The model will be further strengthened by implementing more clinical expertise in dental implant treatment planning and incorporating multiple slices to classify 3-dimensional implant-ridge relationships.

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.

The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review

Implant dentistry has witnessed a transformative shift with the integration of artificial intelligence (AI) technologies. This article explores the role of AI in implant dentistry, emphasizing its impact on diagnostics, treatment planning, and patient outcomes. AI-driven image analysis and deep learning algorithms enhance the precision of implant placement, reducing risks and optimizing aesthetics. Moreover, AI-driven data analytics provide valuable insights into patient-specific treatment strategies, improving overall success rates. As AI continues to evolve, it promises to reshape the landscape of implant dentistry and lead in an era of personalized and efficient oral healthcare.

SinusC-Net for automatic classification of surgical plans for maxillary sinus augmentation using a 3D distance-guided network

The objective of this study was to automatically classify surgical plans for maxillary sinus floor augmentation in implant placement at the maxillary posterior edentulous region using a 3D distance-guided network on CBCT images. We applied a modified ABC classification method consisting of five surgical approaches for the deep learning model. The proposed deep learning model (SinusC-Net) consisted of two stages of detection and classification according to the modified classification method. In detection, five landmarks on CBCT images were automatically detected using a volumetric regression network; in classification, the CBCT images were automatically classified as to the five surgical approaches using a 3D distance-guided network. The mean MRE for landmark detection was 0.87 mm, and SDR for 2 mm or lower, 95.47%. The mean accuracy, sensitivity, specificity, and AUC for classification by the SinusC-Net were 0.97, 0.92, 0.98, and 0.95, respectively. The deep learning model using 3D distance-guidance demonstrated accurate detection of 3D anatomical landmarks, and automatic and accurate classification of surgical approaches for sinus floor augmentation in implant placement at the maxillary posterior edentulous region.

Deep learning-based prediction of osseointegration for dental implant using plain radiography

BACKGROUND

In this study, we investigated whether deep learning-based prediction of osseointegration of dental implants using plain radiography is possible.

METHODS

Panoramic and periapical radiographs of 580 patients (1,206 dental implants) were used to train and test a deep learning model. Group 1 (338 patients, 591 dental implants) included implants that were radiographed immediately after implant placement, that is, when osseointegration had not yet occurred. Group 2 (242 patients, 615 dental implants) included implants radiographed after confirming successful osseointegration. A dataset was extracted using random sampling and was composed of training, validation, and test sets. For osseointegration prediction, we employed seven different deep learning models. Each deep-learning model was built by performing the experiment 10 times. For each experiment, the dataset was randomly separated in a 60:20:20 ratio. For model evaluation, the specificity, sensitivity, accuracy, and AUROC (Area under the receiver operating characteristic curve) of the models was calculated.

RESULTS

The mean specificity, sensitivity, and accuracy of the deep learning models were 0.780-0.857, 0.811-0.833, and 0.799-0.836, respectively. Furthermore, the mean AUROC values ranged from to 0.890-0.922. The best model yields an accuracy of 0.896, and the worst model yields an accuracy of 0.702.

CONCLUSION

This study found that osseointegration of dental implants can be predicted to some extent through deep learning using plain radiography. This is expected to complement the evaluation methods of dental implant osseointegration that are currently widely used.

Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network

Tissue differentiation varies based on patients' conditions, such as occlusal force and bone properties. Thus, the design of the implants needs to take these conditions into account to improve osseointegration. However, the efficiency of the design procedure is typically not satisfactory and needs to be significantly improved. Thus, a deep learning network (DLN) is proposed in this study. A data-driven DLN consisting of U-net, ANN, and random forest models was implemented. It serves as a surrogate for finite element analysis and the mechano-regulation algorithm. The datasets include the history of tissue differentiation throughout 35 days with various levels of occlusal force and bone properties. The accuracy of day-by-day tissue differentiation prediction in the testing dataset was 82%, and the AUC value of the five tissue phenotypes (fibrous tissue, cartilage, immature bone, mature bone, and resorption) was above 0.86, showing a high prediction accuracy. The proposed DLN model showed the robustness for surrogating the complex, time-dependent calculations. The results can serve as a design guideline for dental implants.