Overjet

Analyzes intraoral dental X-rays using computer vision to automatically identify and flag cavities, bone loss, and other dental pathologies. Provides real-time detection results with confidence scores and anatomical localization.

Processes panoramic (full-mouth) dental X-rays to detect periodontal disease, bone resorption, and systemic dental pathologies across the entire dentition. Provides comprehensive overview analysis with multi-tooth assessment.

Aggregates de-identified data across patient cases to provide practice-level insights on diagnostic patterns, disease prevalence, treatment recommendations, and clinical outcomes. Enables comparison against peer benchmarks.

Provides clinically validated AI analysis results that have been benchmarked against experienced radiologist interpretations. Offers confidence that AI detections meet or exceed human expert standards for specific pathologies.

Generates structured clinical reports from AI analysis results, including findings, severity classifications, and treatment recommendations. Reports are formatted for both clinician review and patient communication.

Creates visual, patient-friendly explanations of dental findings with annotated images and simplified language. Helps patients understand diagnoses, treatment necessity, and clinical recommendations.

Evaluates incoming dental X-ray images for quality, positioning, and artifacts before analysis. Flags images that are too degraded or poorly positioned for accurate AI analysis.

Compares dental X-rays from different time periods to track disease progression or treatment response. Identifies changes in pathology severity, bone levels, and other measurable parameters over time.

Seamlessly integrates AI analysis results into existing dental practice management software, automatically populating patient records, treatment plans, and clinical notes. Enables workflow automation without manual data entry.

Processes multiple dental X-ray images in batch mode, analyzing entire patient imaging sets or multiple patients' images simultaneously. Provides aggregated results and comparative analysis across batches.

Provides confidence scores for each AI detection, indicating the model's certainty about identified pathologies. Helps clinicians prioritize findings and identify cases requiring additional review or alternative imaging.

Generates evidence-based treatment recommendations based on AI-detected pathologies, severity levels, and clinical guidelines. Provides prioritized treatment plans with rationale for clinical decision support.