Towards Trustworthy AI in Dentistry

Ma, Jackie; Schneider, Lisa; Lapuschkin, Sebastian; Achtibat, R; Duchrau, Martha; Krois, Joachim; Schwendicke, Falk; Samek, Wojciech

doi:10.1177/00220345221106086

Cited by 31 publications

(14 citation statements)

References 16 publications

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“…Therefore, we see future efforts advancing beyond the methodological limitations of this work by establishing consistent benchmarks and widely accepted systematic annotation schemes, as already suggested in Ma et al (2022) . In addition, with the recent development of generative modeling in computer vision ( Pang et al 2021 ), we like to highlight the potential of infusing OPG images with 3-dimensional information, enabling a highly robust and fine-grained estimation of the relationship between the M3M and the IAN.…”

Section: Discussionmentioning

confidence: 70%

“…Our proposed evaluation strategy was developed to identify and correct overly optimistic model performance both through robust evaluation metrics that are resilient to unbalanced label distribution and through an external validation data set that could potentially detect assimilated shortcut features. As highlighted in the medical imaging domain ( Shehab et al 2022 ), and in particular in the field of dentistry ( Schwendicke et al 2020 ; Ma et al 2022 ), such benchmarking proved essential for trustworthy machine learning. As expected, we observed an overall drop in all model performances when models were evaluated by in-distribution cross-validation in comparison to the out-of-distribution control data set.…”

Section: Discussionmentioning

confidence: 99%

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Preinterventional Third-Molar Assessment Using Robust Machine Learning

Carvalho,

Lotz,

Rubi

et al. 2023

J Dent Res

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Machine learning (ML) models, especially deep neural networks, are increasingly being used for the analysis of medical images and as a supporting tool for clinical decision-making. In this study, we propose an artificial intelligence system to facilitate dental decision-making for the removal of mandibular third molars (M3M) based on 2-dimensional orthopantograms and the risk assessment of such a procedure. A total of 4,516 panoramic radiographic images collected at the Center of Dental Medicine at the University of Zurich, Switzerland, were used for training the ML model. After image preparation and preprocessing, a spatially dependent U-Net was employed to detect and retrieve the region of the M3M and inferior alveolar nerve (IAN). Image patches identified to contain a M3M were automatically processed by a deep neural network for the classification of M3M superimposition over the IAN (task 1) and M3M root development (task 2). A control evaluation set of 120 images, collected from a different data source than the training data and labeled by 5 dental practitioners, was leveraged to reliably evaluate model performance. By 10-fold cross-validation, we achieved accuracy values of 0.94 and 0.93 for the M3M–IAN superimposition task and the M3M root development task, respectively, and accuracies of 0.9 and 0.87 when evaluated on the control data set, using a ResNet-101 trained in a semisupervised fashion. Matthew’s correlation coefficient values of 0.82 and 0.75 for task 1 and task 2, evaluated on the control data set, indicate robust generalization of our model. Depending on the different label combinations of task 1 and task 2, we propose a diagnostic table that suggests whether additional imaging via 3-dimensional cone beam tomography is advisable. Ultimately, computer-aided decision-making tools benefit clinical practice by enabling efficient and risk-reduced decision-making and by supporting less experienced practitioners before the surgical removal of the M3M.

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Section: Discussionmentioning

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Preinterventional Third-Molar Assessment Using Robust Machine Learning

Carvalho,

Lotz,

Rubi

et al. 2023

J Dent Res

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“…These issues are particularly prevalent in LMICs. This lack of transparency raises questions about the quality and reliability of the datasets, which are critical for development and validation of AI algorithms in dentistry [ 3 , 36 , 45 ]. Therefore, it is important for researchers in LMICs to focus on sourcing high-quality, representative datasets, curation, and annotation methods to improve the development and validation of AI algorithms in dentistry, even though it be challenging and time-consuming in a resource-limited setting.…”

Section: Discussionmentioning

confidence: 99%

Research and application of artificial intelligence in dentistry from lower-middle income countries – a scoping review

Umer,

Adnan,

Lal

2024

BMC Oral Health

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Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.

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“…Computational methods such as artificial intelligence and machine learning are emerging in oral health care to solve these diagnostic and prognostic challenges [ 9 ]. Machine learning, which is a subset of artificial intelligence, refers to computationally intensive methods that use data-driven approaches to develop models that require fewer modeling decisions by the modeler than traditional modeling techniques [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Hasuike

Watanabe

Wakuda

et al. 2022

JPM

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Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.

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Towards Trustworthy AI in Dentistry

Cited by 31 publications

References 16 publications

Preinterventional Third-Molar Assessment Using Robust Machine Learning

Preinterventional Third-Molar Assessment Using Robust Machine Learning

Research and application of artificial intelligence in dentistry from lower-middle income countries – a scoping review

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

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