Developing Artificial Intelligence Prototype for Classifying Crude Stingless Bee Propolis towards Innovation in Dentistry
DOI:
https://doi.org/10.12974/2311-8695.2025.13.04Keywords:
Artificial Intelligence, Nutraceutical, Propolis, PrototypeAbstract
Introduction: Stingless bee propolis demonstrates excellent therapeutic properties and its innovation in dentistry. Due to species-specific variations in chemical composition, the nutraceutical potential of each propolis type may be differ.
Purpose: To create an Artificial Intelligence (AI) prototype to classify three different propolis namely Geniotrigona thoracica, Heterotrigona itama, and Tetrigona apicalis, using crude macerated, SEM images.
Methods and Materials; An AI prototype was developed with AI model to perform propolis classification using crude macerated SEM images, for each species. Fine-tuning on the number of images and the number of epochs of the AI model is performed to achieve high performance, which will then use to develop a prototype.
Results: Initial experiment with a small dataset achieved accuracy of 0.67. After augmenting the dataset with 50 additional images per species, accuracy improved to 0.86 after 80 epochs. Other metric evaluations such as precision, recall and F1 score have also been applied to measure the AI model’s performance. The results demonstrate that as the size of the training images and epoch increase, the classification performance is improved. This model has been utilized to develop a prototype with graphical user interface that allows user to insert a new macerated SEM images and the prototype will reveal the propolis species. Expert verification of this prototype has been performed, for use to classify the propolis.
Conclusion: The accuracy of propolis image classification can be further enhanced by using a larger dataset and a higher epoch value. The finding of this study can be used as quality control procedures to profile stingless bee propolis by improving the selection and enriching sustainable sources, as nutraceuticals, employed in dentistry. Future work include the utilization of colour images of crude macerated propolis.
References
Lavinas, F. C., Macedo, E. H. B. C., Sá, G. B. L., Amaral, A. C. F., Silva, J. R. A., Azevedo, M. M. B., Vieira, B. A., Domingos, T. F. S., Vermelho, A. B., Carneiro, C. S., & Rodrigues, I. A. (2019). Brazilian stingless bee propolis and geopropolis: promising sources of biologically active compounds. Revista Brasileira de Farmacognosia, 29(3), 389-399. https://doi.org/10.1016/j.bjp.2018.11.007
Al-Masoodi, O., Said Gulam Khan, H., Baharuddin, I., & Ismail, I. (2022). In-vitro Comparison of Antibacterial Activities on Stingless Bee Propolis using Selected Extraction Methods. Compendium of Oral Science, 9(2), 23-43. https://doi.org/10.24191/cos.v9i2.19230
Ibrahim, N., Zakaria, A. J., Ismail, Z., & Mohd, K. S. (2016). Antibacterial and phenolic content of propolis produced by two Malaysian stingless bees, Heterotrigona itama and Geniotrigona thoracica. International Journal of Pharmacognosy and Phytochemical Research, 8(1).
Pereira, F. A. N., Barboza, J. R., Vasconcelos, C. C., Lopes, A. J. O., & Ribeiro, M. N. de S. (2021). Use of Stingless Bee Propolis and Geopropolis against Cancer—A Literature Review of Preclinical Studies. Pharmaceuticals, 14(11), 1161. https://doi.org/10.3390/ph14111161
Shi, B., Zhao, Y., & Yuan, X. (2019). Effects of MTA and Brazilian propolis on the biological properties of dental pulp cells. Brazilian Oral Research, 33. https://doi.org/10.1590/1807-3107bor-2019.vol33.0117
Salleh, S. N. A. S., Hanapiah, N. A. M., Johari, W. L. W., Ahmad, H., & Osman, N. H. (2021). Analysis of bioactive compounds and chemical composition of Malaysian stingless bee propolis water extracts. Saudi Journal of Biological Sciences, 28(12), 6705-6710. https://doi.org/10.1016/j.sjbs.2021.07.049
Chen, X., Daliri, E. B.-M., Kim, N., Kim, J.-R., Yoo, D., & Oh, D.-H. (2020). Microbial Etiology and Prevention of Dental Caries: Exploiting Natural Products to Inhibit Cariogenic Biofilms. Pathogens, 9(7), 569. https://doi.org/10.3390/pathogens9070569
Demir, S., Aliyazicioglu, Y., Turan, I., Misir, S., Mentese, A., Yaman, S. O., Akbulut, K., Kilinc, K., & Deger, O. (2016). Antiproliferative and proapoptotic activity of Turkish propolis on human lung cancer cell line. Nutrition and Cancer, 68(1), 165-172. https://doi.org/10.1080/01635581.2016.1115096
Przybyłek, I., & Karpiński, T. M. (2019). Antibacterial Properties of Propolis. Molecules, 24(11), 2047. https://doi.org/10.3390/molecules24112047
Bærøe, K., Miyata-Sturm, A., & Henden, E. (2020). How to achieve trustworthy artificial intelligence for health. Bulletin of the World Health Organization, 98(4), 257-262. https://doi.org/10.2471/BLT.19.237289
Le, T.-N., Le, T.-M.-T., Phan, T.-T.-H., Nguyen, H.-D., & Le, T.-L. (2023). A Novel Convolutional Neural Network Architecture for Pollen-Bearing Honeybee Recognition. International Journal of Advanced Computer Science and Applications, 14(8). https://doi.org/10.14569/IJACSA.2023.01408112
Leme, M. H. C., Simm, V. S., Tanno, D. R., Costa, Y. M. G., & Domingues, M. A. (2024). Stingless Bee Classification: A New Dataset and Baseline Results. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (pp. 730-744). Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_52
Wang, W., Li, Y., Zou, T., Wang, X., You, J., & Luo, Y. (2020). A Novel Image Classification Approach via Dense-MobileNet Models. Mobile Information Systems, 2020, 1-8. https://doi.org/10.1155/2020/8836195
Alnuaim, A. A., Zakariah, M., Alhadlaq, A., Shashidhar, C., Hatamleh, W. A., Tarazi, H., Shukla, P. K., & Ratna, R. (2022). Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks. Computational Intelligence and Neuroscience, 2022, 1-16. https://doi.org/10.1155/2022/7463091
Klinger, N. (2024, May 6). MobileNet - Efficient Deep Learning for Mobile Vision. Viso.Ai. https://viso.ai/deep-learning/mobilenet-efficient-deep-learning-for-mobile-vision/
Su, J., Faraone, J., Liu, J., Zhao, Y., Thomas, D. B., Leong, P. H. W., & Cheung, P. Y. K. (2018). Redundancy-reduced MobileNet acceleration on reconfigurable logic for ImageNet classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10824 LNCS, 16-28. https://doi.org/10.1007/978-3-319-78890-6_2
Phiphiphatphaisit, S., & Surinta, O. (2020). Food Image Classification with Improved MobileNet Architecture and Data Augmentation. ACM International Conference Proceeding Series, 51-56. https://doi.org/10.1145/3388176.3388179
Wang, K. (2021). An Overview of Deep Learning Based Small Sample Medical Imaging Classification. 2021 International Conference on Signal Processing and Machine Learning (CONF-SPML), 278-281. https://doi.org/10.1109/CONF-SPML54095.2021.00060
Safonova, A., Ghazaryan, G., Stiller, S., Main-Knorn, M., Nendel, C., & Ryo, M. (2023). Ten deep learning techniques to address small data problems with remote sensing. International Journal of Applied Earth Observation and Geoinformation, 125, 103569. https://doi.org/10.1016/j.jag.2023.103569