ToMoBrush: Exploring Dental Health Sensing using a Sonic Toothbrush
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914945327693824 |
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| author | Yuan, Kuang Ibrahim, Mohamed Song, Yiwen Deng, Guoxiang Vijayan, Suvendra Nerone, Robert Gadre, Akshay Kumar, Swarun |
| author_facet | Yuan, Kuang Ibrahim, Mohamed Song, Yiwen Deng, Guoxiang Vijayan, Suvendra Nerone, Robert Gadre, Akshay Kumar, Swarun |
| contents | Early detection of dental disease is crucial to prevent adverse outcomes. Today, dental X-rays are currently the most accurate gold standard for dental disease detection. Unfortunately, regular X-ray exam is still a privilege for billions of people around the world. In this paper, we ask: "Can we develop a low-cost sensing system that enables dental self-examination in the comfort of one's home?"
This paper presents ToMoBrush, a dental health sensing system that explores using off-the-shelf sonic toothbrushes for dental condition detection. Our solution leverages the fact that a sonic toothbrush produces rich acoustic signals when in contact with teeth, which contain important information about each tooth's status. ToMoBrush extracts tooth resonance signatures from the acoustic signals to characterize varied dental health conditions of the teeth. We evaluate ToMoBrush on 19 participants and dental-standard models for detecting common dental problems including caries, calculus, and food impaction, achieving a detection ROC-AUC of 0.90, 0.83, and 0.88 respectively. Interviews with dental experts validate ToMoBrush's potential in enhancing at-home dental healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01933 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ToMoBrush: Exploring Dental Health Sensing using a Sonic Toothbrush Yuan, Kuang Ibrahim, Mohamed Song, Yiwen Deng, Guoxiang Vijayan, Suvendra Nerone, Robert Gadre, Akshay Kumar, Swarun Audio and Speech Processing Sound J.3; C.3; H.5.2 Early detection of dental disease is crucial to prevent adverse outcomes. Today, dental X-rays are currently the most accurate gold standard for dental disease detection. Unfortunately, regular X-ray exam is still a privilege for billions of people around the world. In this paper, we ask: "Can we develop a low-cost sensing system that enables dental self-examination in the comfort of one's home?" This paper presents ToMoBrush, a dental health sensing system that explores using off-the-shelf sonic toothbrushes for dental condition detection. Our solution leverages the fact that a sonic toothbrush produces rich acoustic signals when in contact with teeth, which contain important information about each tooth's status. ToMoBrush extracts tooth resonance signatures from the acoustic signals to characterize varied dental health conditions of the teeth. We evaluate ToMoBrush on 19 participants and dental-standard models for detecting common dental problems including caries, calculus, and food impaction, achieving a detection ROC-AUC of 0.90, 0.83, and 0.88 respectively. Interviews with dental experts validate ToMoBrush's potential in enhancing at-home dental healthcare. |
| title | ToMoBrush: Exploring Dental Health Sensing using a Sonic Toothbrush |
| topic | Audio and Speech Processing Sound J.3; C.3; H.5.2 |
| url | https://arxiv.org/abs/2402.01933 |