Skip to main content
Springer Nature Link
Log in

A smart home dental care system: integration of deep learning, image sensors, and mobile controller

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

In this study, a home dental care system consisting of an oral image acquisition device and deep learning models for maxillary and mandibular teeth images is proposed. The presented method not only classifies tooth diseases, but also determines whether a professional dental treatment (NPDT) is required. Additionally, a specially designed oral image acquisition device was developed to perform image acquisition of maxillary and mandibular teeth. Two evaluation metrics, namely, tooth disease and NPDT classifications, were examined using 610 compounded and 5251 tooth images annotated by an experienced dentist with a Doctor of Dental Surgery and another dentist with a Doctor of Dental Medicine. In the tooth disease and NPDT classifications, the proposed system showed accuracies greater than 96% and 89%, respectively. Based on these results, we believe that the proposed system will allow users to effectively manage their dental health by detecting tooth diseases by providing information on the need for dental treatment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ali RB, Ejbali R, Zaied M (2016) Detection and classification of dental caries in x-ray images using deep neural networks. In: International conference on software engineering advances (ICSEA), p 236

  • Balaji Ganesh S, Sugumar K (2021) Internet of things–a novel innovation in dentistry. J Adv Oral Res 12(1):42–48

    Article  Google Scholar 

  • Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schürmann F, Krejci I, Markram H (2019) Caries detection with near-infrared transillumination using deep learning. J Dent Res 98(11):1227–1233

    Article  Google Scholar 

  • Chen M, Li W, Hao Y, Qian Y, Humar I (2018) Edge cognitive computing based smart healthcare system. Future Gener Comput Syst 86:403–411

    Article  Google Scholar 

  • Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH (2019) A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep 9(1):1–11

    Google Scholar 

  • Contrera J (2020) Patients in pain, dentists in distress: in a pandemic, the problem with teeth. Washington Post

  • Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. In: Proceedings of the 23rd international conference on machine learning, pp 233–240

  • Dimitrov DV (2016) Medical internet of things and big data in healthcare. Healthc Inform Res 22(3):156

    Article  Google Scholar 

  • Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303–338

    Article  Google Scholar 

  • Giriraju A, Lakshminarayan N (2017) Dental home: a concept for early and everlasting smile. Sch J Dent Sci 4(3):121–124

    Google Scholar 

  • Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  • Hossin M, Sulaiman M (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5(2):1

    Article  Google Scholar 

  • Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  • Hwang JJ, Jung YH, Cho BH, Heo MS (2019) An overview of deep learning in the field of dentistry. Imaging Sci Dent 49(1):1–7

    Article  Google Scholar 

  • James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A et al (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. Lancet 392(10159):1789–1858

    Article  Google Scholar 

  • Kim J, Lee HS, Song IS, Jung KH (2019) DeNTNet: deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep 9(1):1–9

    Google Scholar 

  • Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference for Learning Representations, pp 1–15

  • Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017a) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125

  • Lin TY, Goyal P, Girshick R, He K, Dollár P (2017b) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988

  • Liu L, Xu J, Huan Y, Zou Z, Yeh SC, Zheng L (2019) A smart dental health-IoT platform based on intelligent hardware, deep learning and mobile terminal. IEEE J Biomed Health Inform 24:898–906

    Article  Google Scholar 

  • Ma N, Zhang X, Zheng HT, Sun J (2018) Shufflenet v2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European conference on computer vision (ECCV), pp 116–131

  • Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, Fujita H (2017) Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med 80:24–29

    Article  Google Scholar 

  • OmniVision (2017) Omnivision cmos 720p (1280x720) HD image sensor. https://www.ovt.com/sensors/OV9732/. Accessed 20 June 2021

  • Park WJ, Park JB (2018) History and application of artificial neural networks in dentistry. Eur J Dent 12(04):594–601

    Article  Google Scholar 

  • Peres MA, Macpherson LM, Weyant RJ, Daly B, Venturelli R, Mathur MR, Listl S, Celeste RK, Guarnizo-Herreño CC, Kearns C et al (2019) Oral diseases: a global public health challenge. Lancet 394(10194):249–260

    Article  Google Scholar 

  • Pérez P, Gangnet M, Blake A (2003) Poisson image editing. In: ACM SIGGRAPH 2003 papers, pp 313–318

  • Petro AB, Sbert C, Morel JM (2014) Multiscale retinex. Image Process On Line 4:71–88

    Article  Google Scholar 

  • Prajapati SA, Nagaraj R, Mitra S (2017) Classification of dental diseases using CNN and transfer learning. In: 2017 5th International symposium on computational and business intelligence (ISCBI), IEEE, pp 70–74

  • Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:180402767

  • Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. arXiv preprint arXiv:150601497

  • Ruder S (2016) An overview of gradient descent optimization algorithms. https://arxiv.org/abs/1609.04747

  • Sangaiah AK, Medhane DV, Bian GB, Ghoneim A, Alrashoud M, Hossain MS (2019a) Energy-aware green adversary model for cyberphysical security in industrial system. IEEE Trans Ind Inform 16(5):3322–3329

    Article  Google Scholar 

  • Sangaiah AK, Medhane DV, Han T, Hossain MS, Muhammad G (2019b) Enforcing position-based confidentiality with machine learning paradigm through mobile edge computing in real-time industrial informatics. IEEE Trans Ind Inform 15(7):4189–4196

    Article  Google Scholar 

  • The Linux Kernel documentation (2021) The linux USB video class (UVC) driver. https://www.kernel.org/doc/html/v4.13/media/v4l-drivers/uvcvideo.html#/. Accessed 20 June 2021

  • Vinayahalingam S, Xi T, Bergé S, Maal T, de Jong G (2019) Automated detection of third molars and mandibular nerve by deep learning. Sci Rep 9(1):1–7

    Article  Google Scholar 

  • World Health Organization (2020) Oral health. https://www.who.int/news-room/fact-sheets/detail/oral-health/. Accessed 20 June 2021

  • Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492–1500

  • Yakubov D, Ward M, Ward B, Raymond GF, Paskhover B (2020) Opinion: an increase in severe, late dental complications might result from reliance on home dental remedies during the covid-19 pandemic. J Oral Maxillofac Surg 78(8):1232–1233

    Article  Google Scholar 

Download references

Funding

This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICAN(ICT Challenge and Advanced Network of HRD) program (IITP-2020-0-01816) supervised by the IITP(Institute of Information & Communications Technology Planning & Evaluation). This research was also supported by the National Research Foundation of Korea (NRF) funded by the Korean Government (NRF-2020R1C1C1004324).

Author information

Authors and Affiliations

  1. Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea

    Dogun Kim & Eunil Park

  2. Department of Dental Biomaterials Science, Dental Research Institute, Seoul National University, Seoul, Republic of Korea

    Jaeho Choi

  3. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, USA

    Sangyoon Ahn

  4. Department of Interaction Science, Sungkyunkwan University, Seoul, Republic of Korea

    Eunil Park

  5. Raon Data, Seoul, Republic of Korea

    Eunil Park

Authors
  1. Dogun Kim
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jaeho Choi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Sangyoon Ahn
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Eunil Park
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Eunil Park.

Ethics declarations

Conflict of interest

The authors have no conflict of interest to declare.

Ethical approval

This study was approved by the Ethical Committee and Institutional Review Board of Sungkyunkwan University (#2020-11-012).

Informed consent

Informed consent was obtained from all patients who provided their tooth images before collection.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 502 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, D., Choi, J., Ahn, S. et al. A smart home dental care system: integration of deep learning, image sensors, and mobile controller. J Ambient Intell Human Comput 14, 1123–1131 (2023). https://doi.org/10.1007/s12652-021-03366-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03366-8

Keywords