Dental implants success prediction by classifier ensemble on imbalanced data

Sabzekar, Mostafa; Namakin, Motahare; Babaki, Hanie Alipoor Shahr; Deldari, Arash; Babaiyan, Vahide

doi:10.1016/j.cmpbup.2021.100021

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…Alharbi and Almutiq 72 introduced the concept of using machine learning algorithms to predict if a patient might need a dental implant. A similar study by Sabzekar et al 73 uses a SMOTE (Synthetic Minority Oversampling Technique) algorithm to predict the success of dental implants. The concept of an algorithm to improve the efficacy of dental rehabilitation is used in the field.…”

Section: Discussionmentioning

confidence: 99%

Optimal Treatment Order With Fibula-Free Flap Reconstruction, Oncologic Treatment, and Dental Implants: A Systematic Review and Meta-Analysis

Sriram,

Njoroge,

Lopez

et al. 2024

Journal of Craniofacial Surgery

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Head and neck cancer (HNC) patients benefit from craniofacial reconstruction, but no clear guidance exists for rehabilitation timing. This meta-analysis aims to clarify the impact of oncologic treatment order on implant survival. An algorithm to guide placement sequence is also proposed in this paper. PubMed, Embase, and Web of Science were searched for studies on HNC patients with ablative and fibula-free flap (FFF) reconstruction surgeries and radiotherapy (RTX). Primary outcomes included treatment sequence, implant survival rates, and RTX dose. Of 661 studies, 20 studies (617 implants, 199 patients) were included. Pooled survival rates for implants receiving >60 Gy RTX were significantly lower than implants receiving < 60 Gy (82.8% versus 90.1%, P=0.035). Placement >1 year after RTX completion improved implant survival rates (96.8% versus 82.5%, P=0.001). Implants receiving pre-placement RTX had increased survival with RTX postablation versus before (91.2% versus 74.8%, P<0.001). One hundred seventy-seven implants were placed only in FFF with higher survival than implants placed in FFF or native bone (90.4% versus 83.5%, P=0.035). Radiotherapy is detrimental to implant survival rates when administered too soon, in high doses, and before tumor resection. A novel evidence-based clinical decision-making algorithm was presented for utilization when determining the optimal treatment order for HNC patients. The overall survival of dental prostheses is acceptable, reaffirming their role as a key component in rehabilitating HNC patients. Considerations must be made regarding RTX dosage, timing, and implant location to optimize survival rates and patient outcomes for improved functionality, aesthetics, and comfort.

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Section: Discussionmentioning

confidence: 99%

Optimal Treatment Order With Fibula-Free Flap Reconstruction, Oncologic Treatment, and Dental Implants: A Systematic Review and Meta-Analysis

Sriram,

Njoroge,

Lopez

et al. 2024

Journal of Craniofacial Surgery

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“…Artificial neural networks are a system of artificial neurons that are interconnected and interact ( Lakhotia and Bresson, 2018 ). The application of artificial intelligence algorithms in dentistry is a promising area of research because it allows the identification of certain patterns from large databases and signals ( Sabzekar et al, 2021 ). Artificial intelligence in dentistry is a method of creating a second cogent opinion, which is based on mathematical decision making and prediction ( Park and Park, 2018 ; Bernauer et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants

Lyakhov

Dolgalev

Lyakhova

et al. 2022

Front. Neuroinform.

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Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early postoperative period remains quite high. In this regard, there is a need to develop new methods for preliminary assessment of the patient’s condition to predict the success of single implant survival. The intensive development of artificial intelligence technologies and the increase in the amount of digital information that is available for analysis make it relevant to develop systems based on neural networks for auxiliary diagnostics and forecasting. Systems based on artificial intelligence in the field of dental implantology can become one of the methods for forming a second opinion based on mathematical decision making and forecasting. The actual clinical evaluation of a particular case and further treatment are carried out by the dentist, and AI-based systems can become an integral part of additional diagnostics. The article proposes an artificial intelligence system for analyzing various patient statistics to predict the success of single implant survival. As the topology of the neural network, the most optimal linear neural network architectures were developed. The one-hot encoding method was used as a preprocessing method for statistical data. The novelty of the proposed system lies in the developed optimal neural network architecture designed to recognize the collected and digitized database of various patient factors based on the description of the case histories. The accuracy of recognition of statistical factors of patients for predicting the success of single implants in the proposed system was 94.48%. The proposed neural network system makes it possible to achieve higher recognition accuracy than similar neural network prediction systems due to the analysis of a large number of statistical factors of patients. The use of the proposed system based on artificial intelligence will allow the implantologist to pay attention to the insignificant factors affecting the quality of the installation and the further survival of the implant, and reduce the percentage of complications at all stages of treatment. However, the developed system is not a medical device and cannot independently diagnose patients. At this point, the neural network system for analyzing the statistical factors of patients can predict a positive or negative outcome of a single dental implant operation and cannot be used as a full-fledged tool for supporting medical decision-making.

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Deep Learning for Identification of Dental Implant Systems Using Radiographic Images—A Pilot Study

Benakatti,

Nayakar,

Anandhalli

2024

Lecture Notes in Networks and Systems

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Dental implants success prediction by classifier ensemble on imbalanced data

Cited by 7 publications

References 43 publications

Optimal Treatment Order With Fibula-Free Flap Reconstruction, Oncologic Treatment, and Dental Implants: A Systematic Review and Meta-Analysis

Optimal Treatment Order With Fibula-Free Flap Reconstruction, Oncologic Treatment, and Dental Implants: A Systematic Review and Meta-Analysis

Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants

Deep Learning for Identification of Dental Implant Systems Using Radiographic Images—A Pilot Study

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