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Joseph Macadaeg Acosta, Alexander Patera Nugraha, Kunhua Yang | Japanese Dental Science Review | (2025)
Primary Link Pubmed DOI Openalex Full text (OA)

Key Takeaways

Plain English Takeaway

Smartphone apps that use artificial intelligence can help spot tooth decay accurately, making dental checkups easier and more available, especially for people who can't easily visit a dentist.

Study Aim

The main goal of this systematic review is to find out how well artificial intelligence (AI) tools that use smartphone photos can detect dental caries (tooth decay). The review also looks at how easy these tools are to use in real dental clinics and community settings. It aims to compare the accuracy of AI-based smartphone imaging with traditional methods, check if these tools are practical for everyday use, and identify what helps or hinders their use in regular dental care. Simply put: The study wants to see if AI-powered smartphone apps can reliably find tooth decay and be used easily in real life.

Study Design

The authors conducted a systematic review following PRISMA-DTA guidelines. They searched five major databases for studies up to March 2025. Fourteen studies were included, covering both clinical and laboratory (in vitro) research. The studies used different AI models, such as YOLO (You Only Look Once), DenseNet, and MobileNet, to analyze smartphone images of teeth. The review compared these AI tools to standard dental exams and measured how well they detected cavities. The authors also checked how easy the tools were to use and if they could work in real-world settings. Simply put: The researchers looked at many studies to see how well AI apps on phones can find cavities compared to regular dentist checks.

Findings

The review shows that AI-powered smartphone imaging tools can accurately detect dental caries, especially when the decay is obvious (cavitated lesions). Some AI models, like enhanced YOLO and DenseNet201, even performed better than less-experienced dentists. These tools are easy to use and could help with dental screening at home or in communities with limited dental care. However, the AI tools are less reliable for spotting early or hidden decay. The authors recommend more research to improve early detection, standardize image quality, and test these tools in real clinics. They also highlight the need for larger, more diverse datasets and better validation to ensure fairness and accuracy. Simply put: AI apps on smartphones work well for finding clear tooth decay, but they need more work to catch early problems and be trusted everywhere.

Abstract

Objectives: This systematic review assesses the diagnostic accuracy, feasibility, and clinical performance of artificial intelligence (AI)-based smartphone imaging tools for detecting dental caries. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) guidelines, five databases: PubMed, Scopus, Web of Science, Embase, and Cochrane Library, were searched up to March 26, 2025. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD420251047689). Diagnostic accuracy and feasibility of AI-driven analysis of smartphone-based dental images for the detection of dental caries were assessed. Risk of bias and applicability were evaluated using QUADAS-2. Results: Fourteen studies met the inclusion criteria. AI models, particularly YOLO variants, DenseNet201, and MobileNetV3, demonstrated high diagnostic accuracy, especially for cavitated lesions, with some outperforming junior dentists. Enhanced YOLO models achieved up to 85.5 % mean average precision. Tools were generally user-friendly and suitable for community or at-home screening. However, sensitivity for early or non-cavitated lesions varied. Conclusion: AI-driven smartphone imaging shows promise as an accessible and reliable tool for caries detection, particularly in low-resource or remote settings. Further research is needed to improve early lesion detection, ensure clinical validation, and support equitable implementation.

Referenced In

Mercedes C.
a month ago
AI Papers and Discussion

🦷 Can Your Smartphone Spot Cavities Before You Do? The Future of Dental Care Fits in Your Pocket

Tooth decay (dental caries) remains one of the most widespread chronic health issues worldwide, affecting people of every age group. Yet millions of people, especially those living in rural areas, only seek help once the pain becomes too much to handle. By then, damage is often severe, costly, and painful to treat.

📖 Read More on the recent report on global oral health by WHO here: ISBN: 978-92-4-006148-4 Global oral health status report: towards universal health coverage for oral health by 2030

BUT What if we could change that with something almost everyone already owns: a smartphone? 📱

Traditionally, dentists rely on visual checks, dental tools, and X-rays to find decay. But as we know, these methods only work well in skilled hands with good equipment.

A recent systematic review highlights how artificial intelligence (AI) combined with smartphone imaging are becoming a powerful tool for early cavity detection. Deep learning models—particularly YOLO variants, DenseNet, and MobileNetV3—are achieving impressive diagnostic accuracy when paired with smartphone-captured dental images.

Some standout findings:

  • YOLOv4 hit 99% sensitivity and 94% specificity for caries detection

  • DenseNet201 achieved 93% accuracy in classifying lesion severity

  • MobileNetV3 delivered 90% accuracy while processing images in just 6 seconds with 90% accuracy — fast enough for real-time use

  • A 2D-3D hybrid CNN reached 96.4% accuracy with 99.1% specificity, all while remaining portable and affordable

Even more exciting? One study found that parents could capture usable images of their children's teeth using smartphones, scoring high on usability scales. This opens doors for home-based screening and early childhood caries prevention in communities where dental visits are rare.

Smartphone-based AI tools could allow:

  • Parents to take photos of their children’s teeth at home

  • Community health workers to screen patients in rural areas

  • Early detection of cavities before pain begins

  • Faster referral to dentists when treatment is actually needed

Of course, at the moment, challenges remain in detecting very hidden lesions and along with dealing with limited datasets, but still the direction is clear.

👉 The authors of this review then point to exploring next-generation approaches like Vision Transformers, MedSAM segmentation models, and federated learning, as our next step forward, with much hope that this can be implemented in the real-world soon.

💬 Would you feel comfortable using an app to check your teeth or your child’s teeth at home? Share your thoughts below!

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