Early detection of dental caries remains a persistent challenge in preventive dentistry, particularly in low-resource settings where delayed diagnoses often lead to advanced interventions and higher treatment costs. In this study, we developed and evaluated a multi-modal artificial intelligence (AI) framework that integrates radiographs and intraoral photographs to improve diagnostic accuracy for early caries detection. Our dataset comprised 6,700 annotated images drawn from institutional dental archives and publicly available sources. Separate deep convolutional neural network (CNN) encoders (ResNet-50 for radiographs and EfficientNet-B4 for intraoral photos) were trained, and features were fused using both late-fusion and attention-based strategies. Experimental results demonstrated that single-modality models achieved moderate accuracy (radiograph-only: 86.7%, intraoral-only: 84.9%), whereas the multi-modal attention fusion model significantly outperformed them, achieving 94.6% accuracy, 95.9% sensitivity, 93.1% specificity, and an AUC-ROC of 0.97 (p < 0.01). These improvements not only enhanced early caries detection but also carried substantial clinical and public health implications, enabling cost-effective preventive interventions. The integration of AI-assisted diagnostics into primary care, rural mobile units, and tele-dentistry platforms offers an accessible and economically sustainable solution to global oral health disparities.

early caries detection, multi-modal AI, radiographs, intraoral photographs, dental imaging, attention fusion, public health dentistry

Bai, W., Suzuki, H., Huang, J., Francis, C., Wang, S., Tarroni, G., ... & Rueckert, D. (2020). A population-based phenome-wide association study of cardiac and aortic structure and function. Nature Medicine, 26(10), 1654–1662. https://doi.org/10.1038/s41591-020-1009-y

Cantu, A. G., Gehrung, S., Krois, J., Chaurasia, A., Rossi, J. G., Gaudin, R., ... & Schwendicke, F. (2020). Detecting caries lesions of different radiographic extension on bitewings using deep learning. Journal of Dentistry, 100, 103425. https://doi.org/10.1016/j.jdent.2020.103425

Ekstrand, K. R., Martignon, S., Ricketts, D. N., & Qvist, V. (2007). Detection and activity assessment of primary coronal caries lesions: A methodologic study. Operative Dentistry, 32(3), 225–235. https://doi.org/10.2341/06-63

Espelid, I., Tveit, A. B., & Erickson, R. L. (2003). Radiographic diagnosis of mineral loss in approximal enamel. Caries Research, 37(1), 2–9. https://doi.org/10.1159/000068229

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z

Featherstone, J. D. B. (2008). Dental caries: A dynamic disease process. Australian Dental Journal, 53(3), 286–291. https://doi.org/10.1111/j.1834-7819.2008.00064.x

Hintze, H., Wenzel, A., & Danielsen, B. (2002). Reliability of visual examination, fiber-optic transillumination, and bite-wing radiography, and reproducibility of direct visual examination for the detection of caries on occlusal surfaces. Caries Research, 36(4), 289–295. https://doi.org/10.1159/000063921

Lee, J. H., Kim, D. H., Jeong, S. N., & Choi, S. H. (2018). Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of Dentistry, 77, 106–111. https://doi.org/10.1016/j.jdent.2018.07.015

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005

Schwendicke, F., 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

Zhang, X., Liang, Y., Li, L., Li, W., & Yang, Y. (2021). Application of deep learning in the detection of enamel caries using intraoral photographs. BMC Oral Health, 21(1), 212. https://doi.org/10.1186/s12903-021-01574-5

Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2021). Unet++: A nested U-Net architecture for medical image segmentation. Pattern Recognition, 107, 107031. https://doi.org/10.1016/j.patcog.2020.107031

Nguyen, A. T. P., & Akter, A. (2025). Intelligent Skincare: AI-Driven Cosmetic Product Recommendation Using Advanced Machine Learning Models. American Journal of Health, Medicine and Nursing Practice, 11(1), 26-35.

PHAN, H. T. N., & AKTER, A. (2024). HYBRID MACHINE LEARNING APPROACH FOR ORAL CANCER DIAGNOSIS AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES. Universal Publication Index e-Library, 63-76.

Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.

Nath, F., Asish, S., Debi, H. R., Chowdhury, M. O. S., Zamora, Z. J., & Muñoz, S. (2023, August). Predicting hydrocarbon production behavior in heterogeneous reservoir utilizing deep learning models. In Unconventional Resources Technology Conference, 13–15 June 2023 (pp. 506-521). Unconventional Resources Technology Conference (URTeC).

Ahmmed, M. J., Rahman, M. M., Das, A. C., Das, P., Pervin, T., Afrin, S., ... & Rahman, N. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. American Research Index Library, 31-44.

Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. (2025). BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES. American Research Index Library, 1-13.

Siddique, M. T., Jamee, S. S., Sajal, A., Mou, S. N., Mahin, M. R. H., Obaid, M. O., ... & Hasan, M. (2025). Enhancing Automated Trading with Sentiment Analysis: Leveraging Large Language Models for Stock Market Predictions. The American Journal of Engineering and Technology, 7(03), 185-195.

Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19

Nguyen, A. T. P., Jewel, R. M., & Akter, A. (2025). Comparative Analysis of Machine Learning Models for Automated Skin Cancer Detection: Advancements in Diagnostic Accuracy and AI Integration. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 15-26.

Nguyen, A. T. P., Shak, M. S., & Al-Imran, M. (2024). ADVANCING EARLY SKIN CANCER DETECTION: A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR MELANOMA DIAGNOSIS USING DERMOSCOPIC IMAGES. International Journal of Medical Science and Public Health Research, 5(12), 119-133.

Phan, H. T. N., & Akter, A. (2025). Predicting the Effectiveness of Laser Therapy in Periodontal Diseases Using Machine Learning Models. The American Journal of Medical Sciences and Pharmaceutical Research, 7(01), 27-37.

Phan, H. T. N. (2024). EARLY DETECTION OF ORAL DISEASES USING MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS AND DIAGNOSTIC ACCURACY. International Journal of Medical Science and Public Health Research, 5(12), 107-118.

Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.

Mohammad Iftekhar Ayub, Biswanath Bhattacharjee, Pinky Akter, Mohammad Nasir Uddin, Arun Kumar Gharami, Md Iftakhayrul Islam, Shaidul Islam Suhan, Md Sayem Khan, & Lisa Chambugong. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141–150. https://doi.org/10.37547/tajet/Volume07Issue04-19

Mazharul Islam Tusher, Han Thi Ngoc Phan, Arjina Akter, Md Rayhan Hassan Mahin, & Estak Ahmed. (2025). A Machine Learning Ensemble Approach for Early Detection of Oral Cancer: Integrating Clinical Data and Imaging Analysis in the Public Health. International Journal of Medical Science and Public Health Research, 6(04), 07–15. https://doi.org/10.37547/ijmsphr/Volume06Issue04-02

Safayet Hossain, Ashadujjaman Sajal, Sakib Salam Jamee, Sanjida Akter Tisha, Md Tarake Siddique, Md Omar Obaid, MD Sajedul Karim Chy, & Md Sayem Ul Haque. (2025). Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems. The American Journal of Engineering and Technology, 7(04), 22–33. https://doi.org/10.37547/tajet/Volume07Issue04-04

Ayub, M. I., Bhattacharjee, B., Akter, P., Uddin, M. N., Gharami, A. K., Islam, M. I., ... & Chambugong, L. (2025). Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems. The American Journal of Engineering and Technology, 7(04), 141-150.

Siddique, M. T., Uddin, M. J., Chambugong, L., Nijhum, A. M., Uddin, M. N., Shahid, R., ... & Ahmed, M. (2025). AI-Powered Sentiment Analytics in Banking: A BERT and LSTM Perspective. International Interdisciplinary Business Economics Advancement Journal, 6(05), 135-147.

Al Mamun, A., Nath, A., Dey, S. K., Nath, P. C., Rahman, M. M., Shorna, J. F., & Anjum, N. (2025). Real-Time Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks: A Deep Learning Framework for Enhanced Cybersecurity. The American Journal of Engineering and Technology, 7(03), 252-261.

Tusher, M. I., Hasan, M. M., Akter, S., Haider, M., Chy, M. S. K., Akhi, S. S., ... & Shaima, M. (2025). Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation. International Journal of Medical Science and Public Health Research, 6(05), 63-72.

Sajal, A., Chy, M. S. K., Jamee, S. S., Uddin, M. N., Khan, M. S., Gharami, A. K., ... & Ahmed, M. (2025). Forecasting Bank Profitability Using Deep Learning and Macroeconomic Indicators: A Comparative Model Study. International Interdisciplinary Business Economics Advancement Journal, 6(06), 08-20.

Paresh Chandra Nath, Md Sajedul Karim Chy, Md Refat Hossain, Md Rashel Miah, Sakib Salam Jamee, Mohammad Kawsur Sharif, Md Shakhaowat Hossain, & Mousumi Ahmed. (2025). Comparative Performance of Large Language Models for Sentiment Analysis of Consumer Feedback in the Banking Sector: Accuracy, Efficiency, and Practical Deployment. Frontline Marketing, Management and Economics Journal, 5(06), 07–19. https://doi.org/10.37547/marketing-fmmej-05-06-02

Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.

Jamee, S. S., Sajal, A., Obaid, M. O., Uddin, M. N., Haque, M. S. U., Gharami, A. K., ... & FARHAN, M. (2025). Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry. International Interdisciplinary Business Economics Advancement Journal, 6(05), 90-101.

Hossain, S., Sajal, A., Jamee, S. S., Tisha, S. A., Siddique, M. T., Obaid, M. O., ... & Haque, M. S. U. (2025). Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems. The American Journal of Engineering and Technology, 7(04), 22-33.

Sajal, A., Chy, M. S. K., Jamee, S. S., Uddin, M. N., Khan, M. S., Gharami, A. K., ... & Ahmed, M. (2025). Forecasting Bank Profitability Using Deep Learning and Macroeconomic Indicators: A Comparative Model Study. International Interdisciplinary Business Economics Advancement Journal, 6(06), 08-20.

Mohammad Iftekhar Ayub, Arun Kumar Gharami, Fariha Noor Nitu, Mohammad Nasir Uddin, Md Iftakhayrul Islam, Alifa Majumder Nijhum, Molay Kumar Roy, & Syed Yezdani. (2025). AI-Driven Demand Forecasting for Multi-Echelon Supply Chains: Enhancing Forecasting Accuracy and Operational Efficiency through Machine Learning and Deep Learning Techniques. The American Journal of Management and Economics Innovations, 7(07), 74–85. https://doi.org/10.37547/tajmei/Volume07Issue07-09

Sharmin Sultana Akhi, Sadia Akter, Md Refat Hossain, Arjina Akter, Nur Nobe, & Md Monir Hosen. (2025). Early-Stage Chronic Disease Prediction Using Deep Learning: A Comparative Study of LSTM and Traditional Machine Learning Models. Frontline Medical Sciences and Pharmaceutical Journal, 5(07), 8–17. https://doi.org/10.37547/medical-fmspj-05-07-02

Deep Learning-Driven Customer Segmentation in Banking: A Comparative Analysis for Real-Time Decision Support. (2025). International Interdisciplinary Business Economics Advancement Journal, 6(08), 9-22. https://doi.org/10.55640/business/volume06issue08-02

Nur Nobe, Md Refat Hossain, MD Sajedul Karim Chy, Md. Emran Hossen, Arjina Akter, & Zerin Akter. (2025). Comparative Evaluation of Machine Learning Algorithms for Forecasting Infectious Diseases: Insights from COVID-19 and Dengue Data. International Journal of Medical Science and Public Health Research, 6(08), 22–33. https://doi.org/10.37547/ijmsphr/Volume06Issue08-05