External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study
Objectives: Periodontal disease is a significant public health concern among older adults due to its relationship with tooth loss and systemic health disease. However, there are numerous barriers that prevent older adults from receiving routine dental care, highlighting the need for innovative scree...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | International Dental Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0020653925000140 |
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| author | Reinhard Chun Wang Chau Andrew Chi Chung Cheng Kaijing Mao Khaing Myat Thu Zhaoting Ling In Meei Tew Tien Hsin Chang Hong Jin Tan Colman McGrath Wai-Lun Lo Richard Tai-Chiu Hsung Walter Yu Hang Lam |
| author_facet | Reinhard Chun Wang Chau Andrew Chi Chung Cheng Kaijing Mao Khaing Myat Thu Zhaoting Ling In Meei Tew Tien Hsin Chang Hong Jin Tan Colman McGrath Wai-Lun Lo Richard Tai-Chiu Hsung Walter Yu Hang Lam |
| author_sort | Reinhard Chun Wang Chau |
| collection | DOAJ |
| description | Objectives: Periodontal disease is a significant public health concern among older adults due to its relationship with tooth loss and systemic health disease. However, there are numerous barriers that prevent older adults from receiving routine dental care, highlighting the need for innovative screening tools at the community level. This pilot study aimed first, to evaluate the accuracy of GumAI, a new mHealth tool that uses AI and smartphones to detect gingivitis, and the user acceptance of personalized oral hygiene instructions provided through the new tool, among older adults in day-care community centers. Methods: Participants were invited from 3 day-care community centers. Intraoral photographs were captured and assessed by both GumAI (test) and a panel consisting of 2 calibrated periodontists and a dentist (benchmark). Mean sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score were calculated to determine GumAI's diagnostic performance in comparison to the benchmark. User acceptance with this tool was assessed using 2 Rasch Theory-based 5-point Likert-type questions. Results: 44 participants were recruited out of 80 invited older adults. GumAI demonstrated a sensitivity of 0.93 and specificity of 0.50 compared to the panel‘s assessments, with a PPV of 0.90 and NPV of 0.56. The accuracy and F1 scores were 0.85 and 0.91, respectively. All participants expressed high acceptance of the process. Conclusion: GumAI demonstrates high sensitivity, PPV, accuracy, and F1 score compared to the panel's assessments but falls relatively short in specificity and NPV. Despite this, the tool was highly accepted by older adults, indicating its potential to enhance gingivitis detection and oral hygiene management in community settings. Further refinements are necessary to improve specificity and validate usability measures. Clinical Relevance: This study may pave the way for broader applications of mHealth systems in community settings, enabling greater health coverage and addressing oral health disparities. |
| format | Article |
| id | doaj-art-a44faba8b34d4a19bbe04ebd403a5370 |
| institution | OA Journals |
| issn | 0020-6539 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Dental Journal |
| spelling | doaj-art-a44faba8b34d4a19bbe04ebd403a53702025-08-20T01:49:08ZengElsevierInternational Dental Journal0020-65392025-06-017531970197810.1016/j.identj.2025.01.008External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot StudyReinhard Chun Wang Chau0Andrew Chi Chung Cheng1Kaijing Mao2Khaing Myat Thu3Zhaoting Ling4In Meei Tew5Tien Hsin Chang6Hong Jin Tan7Colman McGrath8Wai-Lun Lo9Richard Tai-Chiu Hsung10Walter Yu Hang Lam11Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, ChinaDepartment of Computer Science, Hong Kong Chu Hai College, Hong Kong Special Administrative Region, ChinaFaculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, ChinaFaculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, ChinaFaculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, ChinaFaculty of Dentistry, The National University of Malaysia, Kuala Lumpur, MalaysiaSchool of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USEastman Dental Institute, University College London, London, UKFaculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, ChinaDepartment of Computer Science, Hong Kong Chu Hai College, Hong Kong Special Administrative Region, ChinaFaculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Computer Science, Hong Kong Chu Hai College, Hong Kong Special Administrative Region, China; Corresponding author: Department of Computer Science, Hong Kong Chu Hai College, 80 Castle Peak Road, Tuen Mun, New Territories, Hong Kong Special Administrative Region, China.Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China; Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong Special Administrative Region, China; Corresponding author: Faculty of Dentistry, The University of Hong Kong, 3/F, Prince Phillip Dental Hospital, 34 Hospital Road, Sai Ying Pun, Hong Kong, Hong Kong Special Administrative Region, China.Objectives: Periodontal disease is a significant public health concern among older adults due to its relationship with tooth loss and systemic health disease. However, there are numerous barriers that prevent older adults from receiving routine dental care, highlighting the need for innovative screening tools at the community level. This pilot study aimed first, to evaluate the accuracy of GumAI, a new mHealth tool that uses AI and smartphones to detect gingivitis, and the user acceptance of personalized oral hygiene instructions provided through the new tool, among older adults in day-care community centers. Methods: Participants were invited from 3 day-care community centers. Intraoral photographs were captured and assessed by both GumAI (test) and a panel consisting of 2 calibrated periodontists and a dentist (benchmark). Mean sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score were calculated to determine GumAI's diagnostic performance in comparison to the benchmark. User acceptance with this tool was assessed using 2 Rasch Theory-based 5-point Likert-type questions. Results: 44 participants were recruited out of 80 invited older adults. GumAI demonstrated a sensitivity of 0.93 and specificity of 0.50 compared to the panel‘s assessments, with a PPV of 0.90 and NPV of 0.56. The accuracy and F1 scores were 0.85 and 0.91, respectively. All participants expressed high acceptance of the process. Conclusion: GumAI demonstrates high sensitivity, PPV, accuracy, and F1 score compared to the panel's assessments but falls relatively short in specificity and NPV. Despite this, the tool was highly accepted by older adults, indicating its potential to enhance gingivitis detection and oral hygiene management in community settings. Further refinements are necessary to improve specificity and validate usability measures. Clinical Relevance: This study may pave the way for broader applications of mHealth systems in community settings, enabling greater health coverage and addressing oral health disparities.http://www.sciencedirect.com/science/article/pii/S0020653925000140Community dentistryGingivitisMachine learningMobile healthPeriodontal diseasesTelemedicine |
| spellingShingle | Reinhard Chun Wang Chau Andrew Chi Chung Cheng Kaijing Mao Khaing Myat Thu Zhaoting Ling In Meei Tew Tien Hsin Chang Hong Jin Tan Colman McGrath Wai-Lun Lo Richard Tai-Chiu Hsung Walter Yu Hang Lam External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study International Dental Journal Community dentistry Gingivitis Machine learning Mobile health Periodontal diseases Telemedicine |
| title | External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study |
| title_full | External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study |
| title_fullStr | External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study |
| title_full_unstemmed | External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study |
| title_short | External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study |
| title_sort | external validation of an ai mhealth tool for gingivitis detection among older adults at daycare centers a pilot study |
| topic | Community dentistry Gingivitis Machine learning Mobile health Periodontal diseases Telemedicine |
| url | http://www.sciencedirect.com/science/article/pii/S0020653925000140 |
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