• Swarali Suryawanshi
• rajeshwari patil
• sakshi andhare
• srushti raut
• Prof. Sonali Potadar

This review paper delves into the realm of automated dental cavity detection, specifically focusing on the machine learning (ML) techniques and their application, within an Android environment. With oral health playing a pivotal role in overall well-being, the fusion of ML algorithms and mobile technology presents a promising avenue for enhancing the accuracy and accessibility of dental cavity diagnosis. The review systematically examines recent advancements in ML-based approaches, spanning traditional algorithms to deep learning models, and assesses their integration into an Android application framework. Key components such as image-based diagnostic tools, data pre-processing techniques, and model architectures are analysed for their efficacy in real-time dental health monitoring. By providing a comprehensive synthesis of existing literature, this review serves as a valuable resource for dental practitioners, researchers, and developers interested in the current landscape of automated dental cavity detection using machine learning, especially when integrated into Android healthcare applications. The insights gained from this review are poised to guide future research directions, fostering advancements at the intersection of dentistry, artificial intelligence, and mobile technology for improved oral healthcare outcomes.

Machine learning, Deep learning, Dental imaging, Android application, Image-based diagnostics, Dental care, Artificial intelligence in dentistry

"Automated Dental Cavity Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.a823-a825, June-2024, Available :http://www.jetir.org/papers/JETIR2406097.pdf

"Automated Dental Cavity Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppa823-a825, June-2024, Available at : http://www.jetir.org/papers/JETIR2406097.pdf