The potential of artificial intelligence in the early detection of systemic diseases during routine dental care

The potential of artificial intelligence in the early detection of systemic diseases during routine dental care

  • Chi A C, Neville B W, Krayer J W, Gonsalves W C. Oral manifestations of systemic disease. Am Fam Physician 2010; 82: 1381-1388.

  • Doughty J, Gallier S M, Paisi M, Witton R, Daley A J. Opportunistic health screening for cardiovascular and diabetes risk factors in primary care dental practices: experiences from a service evaluation and a call to action. Br Dent J 2023; 235: 727-733.

  • Dief S, Veitz-Keenan A, Amintavakoli N, McGowan R. A systematic review on incidental findings in cone beam computed tomography (CBCT) scans. Dentomaxillofac Radiol 2019; 48: 20180396.

  • Alonso M B C C, Vasconcelos T V, Lopes L J, Watanabe P C A, Freitas D Q. Validation of cone-beam computed tomography as a predictor of osteoporosis using the Klemetti classification. Braz Oral Res 2016; DOI: 10.1590/1807-3107BOR-2016.vol30.0073.

  • Drodge D R, Staines K, Shipley D. Skin cancer – what general dental practitioners should look for. Br Dent J 2024; 236: 279-283.

  • Glick M. Screening for traditional risk factors for cardiovascular disease: a review for oral health care providers. J Am Dent Assoc 2002; 133: 291-300.

  • Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J 2021; DOI: 10.7861/fhj.2021-0095.

  • Ghaffari M, Zhu Y, Shrestha A. A review of advancements of artificial intelligence in dentistry. Dent Rev 2024; 4: 100081.

  • World Health Organization International Agency for Research on Cancer. Skin cancer. Available at (accessed 1 July 2025).

  • Roky A H, Islam M M, Ahasan A M F et al. Overview of skin cancer types and prevalence rates across continents. Cancer Pathog Ther 2024; 3: 89-100.

  • National Cancer Institute. Metastatic cancer. Available at (accessed 17 March 2025).

  • American Cancer Society. Can basal and squamous cell skin cancers be found early? 2023. Available at (accessed 1 July 2025).

  • Narayanamurthy V, Padmapriya P, Noorasafrin A et al. Skin cancer detection using non-invasive techniques. RSC Adv 2018; 8: 28095-28130.

  • Melarkode N, Srinivasan K, Qaisar S M, Plawiak P. AI-powered diagnosis of skin cancer: a contemporary review, open challenges and future research directions. Cancers 2023; 15: 1183.

  • Mahmoud N M, Soliman A M. Early automated detection system for skin cancer diagnosis using artificial intelligent techniques. Sci Rep 2024; 14: 9749.

  • Salinas M P, Sepúlveda J, Hidalgo L et al. A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis. NPJ Digit Med 2024; 7: 125.

  • Krakowski I, Kim J, Cai Z R et al. Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis. NPJ Digit Med 2024; 7: 78.

  • Okita A L, de Sousa R M, Rivero-Zavala E J et al. Development of an AI-based skin cancer recognition model and its application in enabling patients to self-triage their lesions with smartphone pictures. Dermato 2024; 4: 97-111.

  • Jemima M M, Kanimozhi J K. Deep learning based patient care mobile application for detecting skin cancer. Int Res J Adv Eng Hub 2024; 2: 14-19.

  • Kränke T, Tripolt-Droschl K, Röd L, Hofmann-Wellenhof R, Koppitz M, Tripolt M. New AI-algorithms on smartphones to detect skin cancer in a clinical setting – a validation study. PLos One 2023; DOI: 10.1371/journal.pone.0280670.

  • Veseli E. Skin cancer and AI. Br Dent J 2024; 236: 581-582.

  • Chiarotti F, Venerosi A. Epidemiology of autism spectrum disorders: a review of worldwide prevalence estimates since 2014. Brain Sci 2020; 10: 274.

  • Maenner M J, Warren Z, Williams A R et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years – autism and developmental disabilities monitoring network, 11 sites, United States, 2020. MMWR Surveill Summ 2023; 72: 1-14.

  • Lopes L T, Rodrigues J M, Baccarin C et al. Autism spectrum as an etiologic systemic disorder: a protocol for an umbrella review. Healthcare (Basel) 2022; 10: 2200.

  • Tsang L P M, How C H, Yeleswarapu S P, Wong C M. Autism spectrum disorder: early identification and management in primary care. Singapore Med J 2019; 60: 324-328.

  • Simeoli R, Rega A, Cerasuolo M, Nappo R, Marocco D. Using machine learning for motion analysis to early detect autism spectrum disorder: a systematic review. Rev J Autism Dev Disord 2024; DOI: 10.1007/s40489-024-00435-4.

  • Kareem A K, A L-Ani M M, Nafea A A. Detection of autism spectrum disorder using a 1-dimensional convolutional neural network. Baghdad Sci J 2023; 20: 1182-1193.

  • Snijder M I J, Langerak I P C, Kaijadoe S P T et al. Parental experiences with early identification and initial care for their child with autism: tailored improvement strategies. J Autism Dev Disord 2022; 52: 3473-3485.

  • Collie A D, Jayaraman J, Carrico C, Upshur C, Bortell E. The age and primary reason for the first dental visit in children with special health care needs. Spec Care Dent 2024; 44: 1107-1114.

  • Reddy K, Taksande A, Kurian B. Harnessing the power of mobile phone technology: screening and identifying autism spectrum disorder with smartphone apps. Cureus 2024; DOI: 10.7759/cureus.55004.

  • Krishnappa Babu P R, Di Martino J M, Aiello R et al. Validation of a mobile app for remote autism screening in toddlers. NEJM AI 2024; DOI: 10.1056/AIcs2400510.

  • Posada-Quintero H F, Manjur S M, Hossain M B et al. Autism spectrum disorder detection using variable frequency complex demodulation of the electroretinogram. Res Autism Spectr Disord 2023; 109: 102258.

  • Veseli E, Krasniqi T P. Early diagnosis of children with autism using artificial intelligence during dental care. Eur Arch Paediatr Dent 2024; 25: 453.

  • Balwan W K, Kour S. A systematic review of hypertension and stress – the silent killers. Scho Acad J Biosci 2021; 9: 150-154.

  • Carey R M, Muntner P, Bosworth H B, Whelton P K. Prevention and control of hypertension: JACC health promotion series. J Am Coll Cardiol 2018; 72: 1278-1293.

  • Begum T, Murrell K, Robinson-Barella A. Tackling inequalities in access to medicines for people experiencing homelessness: a meta-ethnography and qualitative systematic review. Health Expect 2024; DOI: 10.1111/hex.70076.

  • Strauss S M, Alfano M C, Shelley D, Fulmer T. Identifying unaddressed systemic health conditions at dental visits: patients who visited dental practices but not general health care providers in 2008. Am J Public Health 2012; 102: 253-255.

  • Engström S, Berne C, Gahnberg L, Svärdsudd K. Efficacy of screening for high blood pressure in dental health care. BMC Public Health 2011; 11: 194.

  • Glick M, Greenberg B L. The potential role of dentists in identifying patients’ risk of experiencing coronary heart disease events. J Am Dent Assoc 2005; 136: 1541-1546.

  • Doble A, Bescos R, Witton R, Shivji S, Ayres R, Brookes Z. A case-finding protocol for high cardiovascular risk in a primary care dental school-model with integrated care. Int J Environ Res Public Health 2023; 20: 4959.

  • Kaur S, Bansal K, Kumar Y. Machine learning-based approaches for automated hypertension detection. In Gupta A (ed) Next Generation Computing and Information Systems. p 223. Florida: CRC Press, 2024.

  • Argha A, Celler B G, Lovell N H. Artificial intelligence based blood pressure estimation from auscultatory and oscillometric waveforms: a methodological review. IEEE Rev Biomed Eng 2022; 15: 152-168.

  • Landry C, Dhamotharan V, Freithaler M et al. A smartphone application toward detection of systolic hypertension in underserved populations. Sci Rep 2024; 14: 15410.

  • Delmotte L, Desebbe O, Alexander B et al. Smartphone-based versus non-invasive automatic oscillometric brachial cuff blood pressure measurements: a prospective method comparison volunteer study. J Pers Med 2023; 14: 15.

  • Haugg F, Elgendi M, Menon C. Assessment of blood pressure using only a smartphone and machine learning techniques: a systematic review. Front Cardiovasc Med 2022; 9: 894224.

  • Islam S M S, Cartledge S, Karmakar C et al. Validation and acceptability of a cuffless wrist-worn wearable blood pressure monitoring device among users and health care professionals: mixed methods study. JMIR Mhealth Uhealth 2019; DOI: 10.2196/14706.

  • Yonel Z, Dietrich T, Gray L, Chapple I. Early case detection of diabetes in dental practice: a missed opportunity. Br Dent J 2023; 235: 667.

  • Hsu Y T, Nair M, Angelov N, Lalla E, Lee C-T. Impact of diabetes on clinical periodontal outcomes following non-surgical periodontal therapy. J Clin Periodontol 2019; 46: 206-217.

  • Balasubramaniyan S, Jeyakumar V, Nachimuthu D S. Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans. Sci Rep 2022; 12: 186.

  • Swartz T. AI model 98% accurate in detecting diseases – just by looking at your tongue. Available at (accessed 1 December 2024).

  • Lutsker G, Sapir G, Shilo S et al. From glucose patterns to health outcomes: a generalizable foundation model for continuous glucose monitor data analysis. 2024; DOI: 10.48550/arXiv.2408.11876.

  • Tangerman A. Halitosis in medicine: a review. Int Dent J 2002; 52: 201-206

  • Amann A, Costello B D L, Miekisch W et al. The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva. J Breath Res 2014; 8: 034001.

  • De Lacy Costello B, Amann A, Al-Kateb H et al. A review of the volatiles from the healthy human body. J Breath Res 2014; 8: 014001.

  • Mathur A, Mehta V, Obulareddy V T, Kumar P. Narrative review on artificially intelligent olfaction in halitosis. J Oral Maxillofac Pathol 2024; 28: 275-283.

  • Nelson J, Vaddi A, Tadinada A. Can convolutional neural networks identify external carotid artery calcifications? Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138: 142-148.

  • Alqutaibi A Y, Algabri R, Ibrahim W I, Alhajj M N, Elawady D. Dental implant planning using artificial intelligence: a systematic review and meta-analysis. J Prosthet Dent 2024; DOI: 10.1016/j.prosdent.2024.03.032.

  • Xiang B, Lu J, Yu J. Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: a systematic review and meta-analysis. J Dent 2024; 146: 105064.

  • Damaskos S, Tsiklakis K, Syriopoulos K, van der Stelt P. Extra- and intra-cranial arterial calcifications in adults depicted as incidental findings on cone beam CT images. Acta Odontol Scand 2015; 73: 202-209.

  • Kadyan V, Vaddi A, Nagpal A, Molina M R, Lurie A G, Tadinada A. Evaluation of cone-beam computed tomography scans to develop a staging method of external carotid artery calcification. J Clin Med 2024; 13: 3189.

  • American Dental Association Council on Scientific Affairs. The use of cone-beam computed tomography in dentistry: an advisory statement from the American Dental Association Council on Scientific Affairs. J Am Dent Assoc 2012; 143: 899-902.

  • Nimmagadda N, Aboian E, Kiang S, Fischer U. The role of artificial intelligence in vascular care. JVS-Vasc Insights 2024; DOI: 10.1016/J.JVSVI.2024.100179.

  • Ajami M, Tripathi P, Ling H, Mahdian M. Automated detection of cervical carotid artery calcifications in cone beam computed tomographic images using deep convolutional neural networks. Diagnostics 2022; 12: 2537.

  • Alajaji S A, Amarin R, Masri R et al. Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138: 162-172.

  • Ahmed A A, Mahdian M. Automated characterization of arterial calcification in dental cone beam computed tomographic images as a risk factor for cardiovascular disease. Oral Surg Oral Med Oral Pathol Oral Radiol 2025; DOI: 10.1016/j.oooo.2024.11.019.

  • Khadivi G, Akhtari A, Sharifi F et al. Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis. Osteoporos Int 2024; 36: 1-19.

  • link

    Leave a Reply

    Your email address will not be published. Required fields are marked *