Artificial Intelligence in Oral and Maxillofacial Diseases: Enhancing Early Detection, Advancing Treatment Methods, and Accelerating the Prevention and Management Process.
Abstract
Background: Oral and Maxillofacial (OMF) diseases are a significant global health challenge. The conventional approach to diagnosis, which bears subjectivity from the clinical and radiographic aspects, can delay treatment stratification. Artificial Intelligence (AI), specifically deep learning, could solve these problems by providing new levels of precision and personalization to OMF medicine.
Aim: The purpose of this review is to summarize and describe the current uses of AI in detecting, treating, and managing OMF diseases, including how machine learning can provide deeper diagnostics, personalized treatment, and predictive analyses.
Methods: A narrative review was conducted using peer-reviewed literature documenting AI in OMF medicine from PubMed, Scopus, IEEE Xplore, and Google Scholar (2018-2024).
Results: Convolutional neural networks continue to exhibit strong performance in the detection and classification of oral and maxillofacial pathologies from images, consistently performing equal to or better than clinicians in these classifications. AI-based systems provide a new level of precision in surgical treatment planning, implantology, and orthodontics. Predictive analytics provide a level of risk stratification and prognosis creation that facilitates the shift from reactionary health management to proactive health management.
Conclusion: AI has begun to reshape the practice of OMF medicine, whether it be through earlier detection, a more precise intervention, or better overall patient outcomes. Challenges do exist, as in all fields, such as real-world data standardization and clinical validation. However, the opportunities for AI to establish a standard of care based on proactive, predictive, and personalized capabilities in conjunction with human capabilities is significant.
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References
Alabi, R. O., Bello, I. O., Youssef, O., Elmusrati, M., Mäkitie, A. A., & Almangush, A. (2021). Utilizing deep machine learning for prognostication of oral squamous cell carcinoma—a systematic review. Frontiers in oral health, 2, 686863. https://doi.org/10.3389/froh.2021.686863
Alharbi, M. T., & Almutiq, M. M. (2022). Prediction of dental implants using machine learning algorithms. Journal of Healthcare Engineering, 2022(1), 7307675. https://doi.org/10.1155/2022/7307675
Benzinger, L., Ursin, F., Balke, W. T., Kacprowski, T., & Salloch, S. (2023). Should Artificial Intelligence be used to support clinical ethical decision-making? A systematic review of reasons. BMC medical ethics, 24(1), 48. https://doi.org/10.1186/s12910-023-00929-6
Hamid, O. H. (2023). Data-centric and model-centric AI: Twin drivers of compact and robust industry 4.0 solutions. Applied Sciences, 13(5), 2753. https://doi.org/10.3390/app13052753
Homsi, K., Snider, V., Kusnoto, B., Atsawasuwan, P., Viana, G., Allareddy, V., ... & Elnagar, M. H. (2023). In-vivo evaluation of artificial intelligence driven remote monitoring technology for tracking tooth movement and reconstruction of 3-dimensional digital models during orthodontic treatment. American Journal of Orthodontics and Dentofacial Orthopedics, 164(5), 690-699 https://doi.org/10.1016/j.ajodo.2023.04.019
Hsiao, T. Y., Ho, Y. C., Chen, M. R., Lee, S. Y., & Sun, C. W. (2021). Disease activation maps for subgingival dental calculus identification based on intelligent dental optical coherence tomography. Translational Biophotonics, 3(3), e202100001. https://doi.org/10.1002/tbio.202100001
James, S. L., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., ... & Briggs, A. M. (2018). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The lancet, 392(10159), 1789-1858. https://doi.org/10.1016/S0140-6736(18)32279-7
Jaskari, J., Sahlsten, J., Järnstedt, J., Mehtonen, H., Karhu, K., Sundqvist, O., ... & Kaski, K. (2020). Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes. Scientific reports, 10(1), 5842. https://doi.org/10.1038/s41598-020-62321-3
Junaid, N., Khan, N., Ahmed, N., Abbasi, M. S., Das, G., Maqsood, A., ... & Heboyan, A. (2022, December). Development, application, and performance of artificial intelligence in cephalometric landmark identification and diagnosis: a systematic review. In Healthcare (Vol. 10, No. 12, p. 2454). MDPI. https://doi.org/10.3390/healthcare10122454
Krois, J., Ekert, T., Meinhold, L., Golla, T., Kharbot, B., Wittemeier, A., ... & Schwendicke, F. (2019). Deep learning for the radiographic detection of periodontal bone loss. Scientific reports, 9(1), 8495. https://doi.org/10.1038/s41598-019-44839-3
Lin, B., Cheng, M., Wang, S., Li, F., & Zhou, Q. (2022). Automatic detection of anteriorly displaced temporomandibular joint discs on magnetic resonance images using a deep learning algorithm. Dentomaxillofacial Radiology, 51(3), 20210341. https://doi.org/10.1259/dmfr.20210341
Li, W., Liang, Y., Zhang, X., Liu, C., He, L., Miao, L., & Sun, W. (2021). A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos. Scientific Reports, 11(1), 16831. https://doi.org/10.1038/s41598-021-96091-3
Miragall, M. F., Knoedler, S., Kauke-Navarro, M., Saadoun, R., Grabenhorst, A., Grill, F. D., ... & Knoedler, L. (2023). Face the future—artificial intelligence in oral and maxillofacial surgery. Journal of clinical medicine, 12(21), 6843. https://doi.org/10.3390/jcm12216843
Morgan, E., Soerjomataram, I., Rumgay, H., Coleman, H. G., Thrift, A. P., Vignat, J., ... & Arnold, M. (2022). The global landscape of esophageal squamous cell carcinoma and esophageal adenocarcinoma incidence and mortality in 2020 and projections to 2040: new estimates from GLOBOCAN 2020. Gastroenterology, 163(3), 649-658. https://doi.org/10.1053/j.gastro.2022.05.054
Musri, N., Christie, B., Ichwan, S. J. A., & Cahyanto, A. (2021). Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review. Imaging science in dentistry, 51(3), 237. https://doi.org/10.5624/isd.20210074
Ozsari, S., Güzel, M. S., Yılmaz, D., & Kamburoğlu, K. (2023). A comprehensive review of artificial intelligence based algorithms regarding temporomandibular joint related diseases. Diagnostics, 13(16), 2700. https://doi.org/10.3390/diagnostics13162700
Pierce, R. L., Van Biesen, W., Van Cauwenberge, D., Decruyenaere, J., & Sterckx, S. (2022). Explainability in medicine in an era of AI-based clinical decision support systems. Frontiers in genetics, 13, 903600. https://doi.org/10.3389/fgene.2022.903600
Poedjiastoeti, W., & Suebnukarn, S. (2018). Application of convolutional neural network in the diagnosis of jaw tumors. Healthcare informatics research, 24(3), 236-241. https://doi.org/10.4258/hir.2018.24.3.236
Ren, L., Liu, L., Wu, Z., Shan, D., Pu, L., Gao, Y., ... & Lai, W. (2022). The predictability of orthodontic tooth movements through clear aligner among first-premolar extraction patients: a multivariate analysis. Progress in Orthodontics, 23(1), 52. https://doi.org/10.1186/s40510-022-00447-y
Rybinski, M., Dai, X., Singh, S., Karimi, S., & Nguyen, A. (2021). Extracting family history information from electronic health records: natural language processing analysis. JMIR Medical Informatics, 9(4), e24020. https://doi.org/10.2196/24020
Santer, M., Kloppenburg, M., Gottfried, T. M., Runge, A., Schmutzhard, J., Vorbach, S. M., ... & Freysinger, W. (2022). Current applications of artificial intelligence to classify cervical lymph nodes in patients with head and neck squamous cell carcinoma—a systematic review. Cancers, 14(21), 5397. https://doi.org/10.3390/cancers14215397
Schwendicke, F., & Krois, J. (2022). Data dentistry: how data are changing clinical care and research. Journal of dental research, 101(1), 21-29. https://doi.org/10.1177/00220345211020265
Schwendicke, F. A., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: chances and challenges. Journal of dental research, 99(7), 769-774. https://doi.org/10.1177/0022034520915714
Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023). The emergence of AI-based wearable sensors for digital health technology: a review. Sensors, 23(23), 9498. https://doi.org/10.3390/s23239498
Shen, K. L., Huang, C. L., Lin, Y. C., Du, J. K., Chen, F. L., Kabasawa, Y., ... & Huang, H. L. (2022). Effects of artificial intelligence‐assisted dental monitoring intervention in patients with periodontitis: a randomized controlled trial. Journal of clinical periodontology, 49(10), 988-998. https://doi.org/10.1111/jcpe.13675
Shujaat, S., Riaz, M., & Jacobs, R. (2023). Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning. Clinical oral investigations, 27(3), 897-906. https://doi.org/10.1007/s00784-022-04706-4
Suh, H. Y., Lee, H. J., Lee, Y. S., Eo, S. H., Donatelli, R. E., & Lee, S. J. (2019). Predicting soft tissue changes after orthognathic surgery: the sparse partial least squares method. The Angle Orthodontist, 89(6), 910-916. https://doi.org/10.2319/120518-851.1
Tonetti, M. S., Bottenberg, P., Conrads, G., Eickholz, P., Heasman, P., Huysmans, M. C., ... & Paris, S. (2017). Dental caries and periodontal diseases in the ageing population: call to action to protect and enhance oral health and well‐being as an essential component of healthy ageing–Consensus report of group 4 of the joint EFP/ORCA workshop on the boundaries between caries and periodontal diseases. Journal of clinical periodontology, 44, S135-S144. https://doi.org/10.1111/jcpe.12681
Uthoff, R. D., Song, B., Sunny, S., Patrick, S., Suresh, A., Kolur, T., ... & Liang, R. (2018). Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. PloS one, 13(12), e0207493. https://doi.org/10.1371/journal.pone.0207493
Warnakulasuriya, S. (2018). Clinical features and presentation of oral potentially malignant disorders. Oral surgery, oral medicine, oral pathology and oral radiology, 125(6), 582-590. https://doi.org/10.1016/j.oooo.2018.03.011
Welikala, R. A., Remagnino, P., Lim, J. H., Chan, C. S., Rajendran, S., Kallarakkal, T. G., ... & Barman, S. A. (2020). Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. Ieee Access, 8, 132677-132693. https://doi.org/10.1109/ACCESS.2020.3010180
Yadalam, P. K., Trivedi, S. S., Krishnamurthi, I., Anegundi, R. V., Mathew, A., Al Shayeb, M., ... & Rajkumar, R. (2022). Machine learning predicts patient tangible outcomes after dental implant surgery. IEEE Access, 10, 131481-131488. https://doi.org/10.1109/ACCESS.2022.3228793
Yang, S. Y., Li, S. H., Liu, J. L., Sun, X. Q., Cen, Y. Y., Ren, R. Y., ... & Liao, W. (2022). Histopathology-based diagnosis of oral squamous cell carcinoma using deep learning. Journal of Dental Research, 101(11), 1321-1327. https://doi.org/10.1177/00220345221089858
Yoon, H. J., Kim, D. R., Gwon, E., Kim, N., Baek, S. H., Ahn, H. W., ... & Kim, S. J. (2022). Fully automated identification of cephalometric landmarks for upper airway assessment using cascaded convolutional neural networks. European Journal of Orthodontics, 44(1), 66-77. https://doi.org/10.1093/ejo/cjab054
Zoabi, A., Redenski, I., Oren, D., Kasem, A., Zigron, A., Daoud, S., ... & Srouji, S. (2022). 3D printing and virtual surgical planning in oral and maxillofacial surgery. Journal of Clinical Medicine, 11(9), 2385. https://doi.org/10.3390/jcm11092385
Authors
Copyright (c) 2024 saud Faisal Abed, Nada Jameel mahmad salamah, Fatin Soud ALsuhaimi, Rabab Mahdi Mustafa Alnakhali, Masudah Zidan Aljohani, Wedyan bakor
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