Cervify provides an AI‑driven software platform that assists pathologists and bioengineers in analyzing cervical biopsy and cytology samples. The tool combines artificial intelligence with cell physiological modeling to automatically identify pathological findings, suggest diagnoses, and generate transparent, explainable reports, while a user‑friendly interface streamlines documentation and reduces analysis time. By aggregating data across samples, Cervify also supports more personalized, longitudinal assessments of cervical health.
Funding
Funding not disclosed
Founders
Product
Problem
Pathologists and bioengineers often spend extensive time manually reviewing cervical biopsy and cytology slides, which can delay diagnosis and introduce variability in interpretation. Limited tools for aggregating data across multiple samples hinder personalized analysis and comprehensive reporting.
Solution
Cervify offers an AI‑driven software platform that automates the identification of pathological findings in cervical tissue samples and proposes diagnostic suggestions. The system combines deep learning with cell physiological modeling to produce transparent, explainable reports that link diagnoses to underlying tissue changes. A unified, user‑friendly interface integrates documentation functions, reducing the manual effort required for each case. By aggregating results across biopsies and cytology samples over time, Cervify enables more individualized analysis and longitudinal tracking of patient data. The platform is designed to fit into existing pathology workflows, allowing clinicians to focus on complex cases while maintaining high diagnostic quality.
Target Audience
Primary users are clinical pathologists and biomedical engineers who analyze cervical biopsy and cytology specimens, as well as diagnostic laboratories seeking to improve efficiency and consistency in cervical cancer screening.
Features
- Deep‑learning models trained on cervical histology and cytology images for automated detection of lesions and abnormalities
- Cell physiological modeling that connects visual findings to underlying tissue pathology, providing explainable diagnostic rationale
- Integrated documentation tools that capture annotations, measurements, and report text within a single interface
- Multi‑sample aggregation engine that consolidates data across visits to support personalized, longitudinal analysis
- Real‑time confidence scores and suggested diagnoses to assist pathologists in decision making
- Compatibility with standard digital pathology file formats and laboratory information systems