Dental Caries Detection with AI | Pearl

Take an in-depth look at what Second Opinion can do for your practice

Detecting dental caries early is a fundamental diagnostic challenge. When you identify demineralization and decay in their earliest forms, you directly impact treatment outcomes, patient comfort, and long-term oral health. Yet traditional visual and radiographic interpretation methods often face limitations: sensitivity is lower for subtle lesions, consistency can vary among clinicians, and recognizing early-stage disease can be challenging.

That’s where Pearl AI transforms caries detection. By applying sophisticated algorithms to radiographic images, the system identifies subtle radiographic changes and supports your clinical decision-making with objective, consistent diagnostic intelligence. With Pearl’s pattern-recognition capabilities, you gain enhanced insight into carious activity across surfaces and stages of disease.

Understanding Pearl AI technology for caries detection

Pearl AI employs deep learning neural networks that have been trained on extensive datasets of annotated dental radiographs. These models are designed to recognize characteristic patterns of carious lesions across different tooth surfaces, stages of progression, and radiographic presentations. This pattern-recognition capability exceeds many human visual-interpretation limitations because it can detect subtle pixel-level cues and integrate anatomical context that might be missed in routine review.

Under the hood, Pearl’s algorithms process pixel-level information, analyze density gradients, evaluate anatomical context (for example, the enamel-dentine junction, pulp chamber proximity, and contact-area geometry), and apply sophisticated classification criteria that identify carious activity with validated sensitivity and specificity. Pearl’s Second Opinion system is FDA cleared for caries detection and other dental pathologies on bitewing and periapical radiographs.

Consequently, when you use the system chairside, you’re not simply reviewing a radiograph as you normally would. Instead, you receive AI-augmented detection overlays and reports that highlight potential lesions for further review, which can help you catch early changes, confirm your interpretations, and ultimately improve diagnostic confidence.

Stages of caries development Pearl AI identifies

Here’s how Pearl AI supports your diagnosis at each stage of disease development.

Initial enamel demineralization and white-spot lesions

In this early stage before cavitation, radiographic signs are subtle, such as slight radiolucency or density changes in the enamel. Pearl AI can highlight these minimal changes, which can go unnoticed in a routine review, giving you advanced warning of incipient enamel lesions. This gives you the opportunity for preventive intervention.

Enamel caries progression and dentinal involvement

Once the caries process approaches or crosses the dentino-enamel junction, the treatment decisions change. You may need to plan for operative intervention rather than purely preventive care. 

Pearl AI helps distinguish between enamel-limited lesions and those with potential dentinal involvement, allowing you to tailor your treatment strategy accordingly.

Dentinal caries and pulpal proximity assessment

With deeper lesions, you’re concerned about the depth of penetration, pulp proximity, and potential for pulpal involvement. Pearl AI evaluates the lesion’s size and position relative to the pulp chamber and provides you with objective data to support your decision-making, whether you proceed with a conservative restoration or consider endodontic referral.

Proximal surface caries in contact areas

Interproximal caries are often tricky: overlapping anatomy, limited visual access, and contact interference make detection difficult by sight alone. Pearl AI brings added sensitivity in those challenging zones, flagging potential lesions between teeth so you can intervene before the lesion becomes extensive.

Occlusal caries in pits and fissures

Occlusal surfaces, especially in fissures and pits, pose another diagnostic challenge because of superimposition and complex anatomy. Pearl AI assists by analyzing the radiograph at pixel-level resolution and identifying subtle density gradients that correlate with occlusal caries-risk areas, alerting you to early defects you might otherwise miss.

Root surface caries and cervical lesions

Root surface and cervical lesions are often underdiagnosed due to thinner enamel/dentin, gingival recession, and anatomical irregularities. Pearl AI extends its detection capabilities into these zones by applying its classification algorithms to root surfaces, cervical demineralization, and areas adjacent to gingival margins, helping you catch lesions that might otherwise be hidden.

Secondary caries around existing restorations

Secondary or recurrent caries around margins and under restorations is a significant clinical concern. Traditional interpretation can struggle with radiopaque restorative materials, shadowing, and margin geometry. 

Pearl AI is trained to recognize subtle changes adjacent to restoration margins; differentiate between normal anatomy, restoration features, and carious progression; and provide you with flagged areas for further examination. This supports both diagnosis and documentation. Detection depends on radiographic visibility and lesion size.

Clinical applications and diagnostic workflow integration

Here’s how you can use Pearl AI in your everyday workflow to support caries detection, treatment planning, and quality assurance.

Initial patient examination

During your new-patient or comprehensive exam, Pearl AI runs in the background and provides you with annotated radiographs (bitewings, periapicals) that highlight potential carious lesions across every tooth surface. This gives you a baseline diagnostic screen, helps establish thorough documentation, and supports your conversation with the patient on areas of concern.

Recall examination and disease monitoring

For recall visits, you can leverage the AI to compare current and prior radiographs, track lesion progression (or stability), and detect any new caries activity. This gives you objective evidence to monitor changes over time, validate your preventive treatments, and identify new lesions early.

Treatment planning and case presentation

When explaining treatment options to a patient, you’ll appreciate how Pearl AI’s visuals and quantitative data support your recommendations. You can use the flagged areas and overlay annotations to illustrate the carious process, show how early intervention can save tooth structure, and document your findings for internal records and insurance communication.

Quality assurance

AI-assisted detection provides a consistent diagnostic standard across clinicians, regardless of experience level or different chairs or sites. You can use Pearl AI to reduce variability, ensure that no zone is overlooked, and support your practice’s quality-assurance programmes. This helps you maintain diagnostic rigor and standardize your caries-detection protocols.

Advantages of AI-assisted caries detection over traditional methods

Here’s how using Pearl AI gives you distinct advantages compared with standard visual and radiographic interpretation alone.

Enhanced detection of early-stage lesions

With Pearl AI, you gain improved sensitivity for signs of incipient carious lesions such as subtle enamel demineralization and early dentinal involvement. The algorithm picks up density gradients, pixel-level cues, and anatomical context that are harder to detect with the naked eye. Modern AI systems can achieve an accuracy rate of over 90% in detecting dental caries.

This earlier detection allows you to intervene sooner, preserve more tooth structure, and potentially shift treatment toward prevention rather than restoration.

Reduced false negatives and missed diagnoses

A key challenge in caries diagnostics is the risk of overlooking lesions, especially in contact areas, root surfaces, or beneath restorations. With Pearl AI acting as a “second pair of eyes”, you reduce interpretation variability and human perceptual error. Pearl’s deep-learning approach picks up hard-to-spot issues like incipient caries or the early signs of a periapical radiolucency.

This gives you more confidence that diagnostic gaps are minimized.

Objective measurement and documentation

Pearl AI doesn’t just flag areas of concern. With Practice Intelligence, you can surface patients with unscheduled care needs and use AI-supported visuals to aid documentation and communication.

That means you have standardized reporting, improved documentation of findings, and better material for treatment discussions with patients or communications with insurers.

Consistent interpretation across practitioners

In a multi-clinician practice or a group-practice environment, interpretation standards vary. Pearl AI ensures consistent algorithmic assessment: the system will deliver the same high-quality analysis every time.

That consistency helps you maintain diagnostic quality, regardless of operator experience level or fatigue.

Clinical evidence and validation studies

Before implementing any diagnostic aid, you want to understand how well it’s been validated. Here’s what the literature says about Pearl AI.

In a large multi-site study of 8,700+ bitewing and periapical radiographs, Pearl’s AI system showed higher accuracy and consistency for caries detection than unaided dentists.

In general, peer-reviewed research supports that AI software enhances sensitivity and specificity in caries detection. For example, one study showed that Second Opinion improved diagnostic accuracy for caries from 82% to 98% when used by operators.

What this means for you: when you adopt Pearl AI, you’re leveraging a tool grounded in evidence showing that algorithm-based detection can outperform unaided visual or radiographic examination, especially in early-stage and subtle lesions.

Pearl AI implementation guidelines for clinical practices

Here are practical considerations for successfully integrating Pearl AI into your practice workflow.

• Technical setup: Ensure your radiographic imaging system is compatible with the Pearl platform. The Pearl platform supports both bitewing and periapical radiographs and is cleared as a computer-aided detection software for these image types. Confirm network/integration requirements and data security protocols (e.g., GDPR if you are in Europe, HIPAA in the US).
• Staff training and adoption: Educate your clinical team on how to interpret AI findings: understand what the algorithm flags, review overlays together, and maintain your clinical judgment. The AI is a support tool, not a replacement for your decision-making. Develop a protocol for reviewing, discussing, and documenting flagged areas with the patient.
• Workflow integration: Incorporate AI analysis into your standard radiographic review. When a radiograph is captured, run the image through Pearl AI and review the flagged results before finalizing your diagnosis. Set up recall workflows where AI analysis from prior visits is compared to current images to monitor progression or stability.
• Quality assurance and continuous review: Use documented AI findings to track diagnostic consistency across cases and clinicians. Periodically review “false positive” and “false negative” flags, validate them clinically, and adjust your protocols accordingly. You can also audit diagnostic outcomes (e.g., restoration on flagged lesions) to refine how you respond to AI findings and integrate them into your recall/prevention strategy.