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Neumayr J, Frenkel E, Schwarzmaier J, Ammar N, Kessler A, Schwendicke F, Kühnisch J, Dujic H. External validation of an artificial intelligence-based method for the detection and classification of molar incisor hypomineralisation in dental photographs. J Dent 2024; 148:105228. [PMID: 38972447 DOI: 10.1016/j.jdent.2024.105228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/29/2024] [Accepted: 07/04/2024] [Indexed: 07/09/2024] Open
Abstract
OBJECTIVES This ex vivo diagnostic study aimed to externally validate an open-access artificial intelligence (AI)-based model for the detection, classification, localisation and segmentation of enamel/molar incisor hypomineralisation (EH/MIH). METHODS An independent sample of web images showing teeth with (n = 277) and without (n = 178) EH/MIH was evaluated by a workgroup of dentists whose consensus served as the reference standard. Then, an AI-based model was used for the detection of EH/MIH, followed by automated classification and segmentation of the findings (test method). The accuracy (ACC), sensitivity (SE), specificity (SP) and area under the curve (AUC) were determined. Furthermore, the correctness of EH/MIH lesion localisation and segmentation was evaluated. RESULTS An overall ACC of 94.3 % was achieved for image-based detection of EH/MIH. Cross-classification of the AI-based class prediction and the reference standard resulted in an agreement of 89.2 % for all diagnostic decisions (n = 594), with an ACC between 91.4 % and 97.8 %. The corresponding SE and SP values ranged from 81.7 % to 92.8 % and 91.9 % to 98.7 %, respectively. The AUC varied between 0.894 and 0.945. Image size had only a limited impact on diagnostic performance. The AI-based model correctly predicted EH/MIH localisation in 97.3 % of cases. For the detected lesions, segmentation was fully correct in 63.4 % of all cases and partially correct in 33.9 %. CONCLUSIONS This study documented the promising diagnostic performance of an open-access AI tool in the detection and classification of EH/MIH in external images. CLINICAL SIGNIFICANCE Externally validated AI-based diagnostic methods could facilitate the detection of EH/MIH lesions in dental photographs.
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Affiliation(s)
- Julia Neumayr
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany
| | - Elisabeth Frenkel
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany
| | - Julia Schwarzmaier
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany
| | - Nour Ammar
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany
| | - Andreas Kessler
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany; Department of Prosthetic Dentistry, Faculty of Medicine, Center for Dental Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Falk Schwendicke
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany
| | - Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany.
| | - Helena Dujic
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany
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Hao B, Hu Y, Adams WG, Assoumou SA, Hsu HE, Bhadelia N, Paschalidis IC. A GPT-based EHR modeling system for unsupervised novel disease detection. J Biomed Inform 2024; 157:104706. [PMID: 39121932 DOI: 10.1016/j.jbi.2024.104706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/17/2024] [Accepted: 08/06/2024] [Indexed: 08/12/2024]
Abstract
OBJECTIVE To develop an Artificial Intelligence (AI)-based anomaly detection model as a complement of an "astute physician" in detecting novel disease cases in a hospital and preventing emerging outbreaks. METHODS Data included hospitalized patients (n = 120,714) at a safety-net hospital in Massachusetts. A novel Generative Pre-trained Transformer (GPT)-based clinical anomaly detection system was designed and further trained using Empirical Risk Minimization (ERM), which can model a hospitalized patient's Electronic Health Records (EHR) and detect atypical patients. Methods and performance metrics, similar to the ones behind the recent Large Language Models (LLMs), were leveraged to capture the dynamic evolution of the patient's clinical variables and compute an Out-Of-Distribution (OOD) anomaly score. RESULTS In a completely unsupervised setting, hospitalizations for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection could have been predicted by our GPT model at the beginning of the COVID-19 pandemic, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 92.2 %, using 31 extracted clinical variables and a 3-day detection window. Our GPT achieves individual patient-level anomaly detection and mortality prediction AUC of 78.3 % and 94.7 %, outperforming traditional linear models by 6.6 % and 9 %, respectively. Different types of clinical trajectories of a SARS-CoV-2 infection are captured by our model to make interpretable detections, while a trend of over-pessimistic outcome prediction yields a more effective detection pathway. Furthermore, our comprehensive GPT model can potentially assist clinicians with forecasting patient clinical variables and developing personalized treatment plans. CONCLUSION This study demonstrates that an emerging outbreak can be accurately detected within a hospital, by using a GPT to model patient EHR time sequences and labeling them as anomalous when actual outcomes are not supported by the model. Such a GPT is also a comprehensive model with the functionality of generating future patient clinical variables, which can potentially assist clinicians in developing personalized treatment plans.
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Affiliation(s)
- Boran Hao
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Yang Hu
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - William G Adams
- Department of Pediatrics, Boston Medical Center, Boston, MA, USA; Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
| | - Sabrina A Assoumou
- Department of Medicine, Boston Medical Center, Boston, MA, USA; Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
| | - Heather E Hsu
- Department of Pediatrics, Boston Medical Center, Boston, MA, USA; Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
| | - Nahid Bhadelia
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA; Center for Emerging Infectious Diseases Policy and Research, Boston University, Boston, MA, USA
| | - Ioannis Ch Paschalidis
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA; Department of Biomedical Engineering, Division of Systems Engineering, Faculty of Computing & Data Sciences, and Hariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, MA, USA.
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Veseli E. Early detection of MIH in children by using artificial intelligence. Eur Arch Paediatr Dent 2024:10.1007/s40368-024-00931-5. [PMID: 39090506 DOI: 10.1007/s40368-024-00931-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 07/02/2024] [Indexed: 08/04/2024]
Affiliation(s)
- E Veseli
- Department of Prosthodontics, Dental School, Faculty of Medicine, University of Pristina, Pristina, Kosovo.
- Department of Dental Research, Center for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
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La Rosa S, Quinzi V, Palazzo G, Ronsivalle V, Lo Giudice A. The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature. Healthcare (Basel) 2024; 12:1311. [PMID: 38998846 PMCID: PMC11240988 DOI: 10.3390/healthcare12131311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. METHODS A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. RESULTS From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. CONCLUSION AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.
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Affiliation(s)
- Salvatore La Rosa
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Quinzi
- Department of Life, Health & Environmental Sciences, Postgraduate School of Orthodontics, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giuseppe Palazzo
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Ronsivalle
- Section of Oral Surgery, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Policlinico Universitario “Gaspare Rodolico—San Marco”, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy;
| | - Antonino Lo Giudice
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
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Dumbryte I, Narbutis D, Androulidaki M, Vailionis A, Juodkazis S, Malinauskas M. Teeth Microcracks Research: Towards Multi-Modal Imaging. Bioengineering (Basel) 2023; 10:1354. [PMID: 38135945 PMCID: PMC10740647 DOI: 10.3390/bioengineering10121354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/16/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023] Open
Abstract
This perspective is an overview of the recent advances in teeth microcrack (MC) research, where there is a clear tendency towards a shift from two-dimensional (2D) to three-dimensional (3D) examination techniques, enhanced with artificial intelligence models for data processing and image acquisition. X-ray micro-computed tomography combined with machine learning allows 3D characterization of all spatially resolved cracks, despite the locations within the tooth in which they begin and extend, and the arrangement of MCs and their structural properties. With photoluminescence and micro-/nano-Raman spectroscopy, optical properties and chemical and elemental composition of the material can be evaluated, thus helping to assess the structural integrity of the tooth at the MC site. Approaching tooth samples having cracks from different perspectives and using complementary laboratory techniques, there is a natural progression from 3D to multi-modal imaging, where the volumetric (passive: dimensions) information of the tooth sample can be supplemented by dynamic (active: composition, interaction) image data. Revelation of tooth cracks clearly shows the need to re-assess the role of these MCs and their effect on the structural integrity and longevity of the tooth. This provides insight into the nature of cracks in natural hard materials and contributes to a better understanding of how bio-inspired structures could be designed to foresee crack propagation in biosolids.
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Affiliation(s)
- Irma Dumbryte
- Institute of Odontology, Vilnius University, LT-08217 Vilnius, Lithuania
| | - Donatas Narbutis
- Institute of Theoretical Physics and Astronomy, Vilnius University, LT-10222 Vilnius, Lithuania
| | - Maria Androulidaki
- Microelectronics Research Group, Institute of Electronic Structure & Laser, Foundation for Research and Technology FORTH-Hellas, 70013 Heraklion, Crete, Greece
| | - Arturas Vailionis
- Stanford Nano Shared Facilities, Stanford University, Stanford, CA 94305, USA
- Department of Physics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania
| | - Saulius Juodkazis
- Optical Sciences Centre and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
- WRH Program International Research Frontiers Initiative (IRFI), Tokyo Institute of Technology, Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan
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