Clinical decision support systems in orthodontics: A narrative review of data science approaches

Turkestani, Najla Al; Bianchi, Jonas; Deleat‐Besson, Romain; Le, Celia; Li, Tengfei; Prieto, Juan Carlos; Gurgel, Marcela; Ruellas, Antônio Carlos de Oliveira; Massaro, Camila; Castillo, Aron Aliaga–Del; Evangelista, Karine; Yatabe, Marília; Benavides, Erika; Soki, Fabiana N.; Zhang, Winston; Najarian, Kayvan; Gryak, Jonathan; Styner, Martin; Fillion‐Robin, Jean‐Christophe; Paniagua, Beatriz; Soroushmehr, S. M. Reza; Cevidanes, Lucia

doi:10.1111/ocr.12492

Cited by 18 publications

(15 citation statements)

References 58 publications

(141 reference statements)

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“…This study's proposed method can help meet the dental field's need for dataenabled innovations and create new areas of research in dental analytics. ML techniques such as text mining, TM and OLR and/or ANN are well-suited for application within a dental practice context and present substantial value through their ability to improve timely access to pertinent patient information and identify important predictors/contributors to oral health outcomes (12,61). ML techniques can be also applied to other health-related fields that record patients' information as large amounts of unstructured texts, such as medicine (65).…”

Section: Discussionmentioning

confidence: 99%

“…It is a powerful research tool that can be used for information retrieval, information extraction, and text categorization (24). Text mining is appropriate for patient chart data as it is useful for analyzing large data sets to extract latent (unknown) patterns in the data to create comprehensible information (12). Further, as text mining extracts key information from textual data that can be used to make sense of large quantities of both relevant and irrelevant information, patient chart data is a suitable application as it consists of textual, unstructured data (i.e., information that either does not have a predefined data model or is not organized in a pre-defined manner) (12).…”

Section: Text Miningmentioning

confidence: 99%

“…Ordinal logistic regression was first computed to reduce dimensionality before ANN analysis. This was conducted prior to ANN to reduce irrelevant and unnecessary data and improve training outcomes and output comprehensibility (12,52). Dimensionality reduction achieved these features through: (1) feature selection, which selects data pertaining to the most relevant information to solve a particular problem, and (2) feature extraction, which transforms the original features into a new, smaller set of more significant features (52).…”

Section: Prediction (Ordinal Logistic Regression and Artificial Neura...mentioning

confidence: 99%

“…In contrast, unsupervised algorithms aim to discover unknown patterns in unlabeled data (i.e., data with no assigned meaning) and do not require human intervention for learning as these algorithms are tasked to identify key information on their own (7,(9)(10)(11). Medical and dental clinicians can utilize ML algorithms to manage and analyze the immense data present in patients' health records to extract meaningful information that can be used to improve population health and wellbeing (12). Diagnostic medicine incorporates advanced AI technologies to predict or diagnose disease and assist in the clinical decision-making process (9)(10)(11), and several studies have shown that AI-based comprehensive care system has great potential to enhance patient care, spur innovative research, and facilitate information sharing (13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

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An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Ameli¹,

Gibson²,

Khanna³

et al. 2022

Front. Dent. Med

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ObjectiveVarious health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.MethodsA secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).ResultsData from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).ConclusionOur study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Text Miningmentioning

confidence: 99%

Section: Prediction (Ordinal Logistic Regression and Artificial Neura...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Ameli¹,

Gibson²,

Khanna³

et al. 2022

Front. Dent. Med

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“…Monill‐González et aland Gili et al review the current status of the application of AI in orthodontic clinics and provide perspectives on this technology 11,12 . AI Turkestani et al reviews the data science approaches for clinical decision support systems in orthodontics and also introduce a web‐based data management platform 13 …”

mentioning

confidence: 99%

Artificial intelligence and machine learning in orthodontics

Yamashiro

2021

Orthod Craniofacial Res

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Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models Using Biological Privileged Information

Al Turkestani,

Cai,

Cevidanes

et al. 2023

Lecture Notes in Computer Science

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This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5fold strati ed cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, speci city and precision of 0.81, 0.79 and 0.77, respectively. foundation for reliable detection of TMJ OA pathological alterations and are potential markers for prediction of disease progression in future longitudinal studies.

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Clinical decision support systems in orthodontics: A narrative review of data science approaches

Cited by 18 publications

References 58 publications

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Artificial intelligence and machine learning in orthodontics

Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models Using Biological Privileged Information

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