We build and commercialize medical AI systems that transform retinal and anterior segment imaging into actionable diagnostic intelligence — for eye disease and beyond.
Is our flagship AI platform, which comprises of bespoke AI solutions, engineered for analyzing specific ocular and oculomics conditions.
Each such solution is independent of each other but shares common architecture that enable ease of use and integration
When you choose FH-POISE AI solutions, you get:
Our flagship AI platform analyzes retinal and anterior segment images to deliver multi-condition diagnostic insights — in real time, at point of care.
Deep CNN classifier trained on 500K+ real-world screenings. Detects microaneurysms, hemorrhages, exudates, and neovascularization. Produces No DR → Proliferative scores with Grad-CAM heatmaps and macular edema co-detection.
Automated optic disc and cup segmentation with cup-to-disc ratio estimation. Identifies RNFL thinning and disc pallor indicative of glaucomatous damage. Outputs suspicion flag with segmentation overlay for referral triage.
Detects early, intermediate, and late AMD by identifying drusen deposits, geographic atrophy, and choroidal neovascularization. Supports dry vs. wet AMD differentiation to guide urgency of referral.
Detects lens opacity and grades cataract severity from retinal images using image quality degradation as proxy signal. Enables opportunistic cataract screening during routine retinal examination — no separate slit-lamp required.
Identifies vascular changes associated with systemic hypertension — arteriovenous nicking, silver/copper wiring, focal arteriolar narrowing. Non-invasive hypertension screening signal for community health programs.
Leverages retinal vascular geometry — fractal dimension, vessel tortuosity, caliber asymmetry — as surrogate markers for CVD risk. Predicts composite cardiovascular risk score from fundus image alone, without blood tests or ECG.
Analyzes interference fringe patterns in the tear film lipid layer. Classifies lipid layer thickness and uniformity as an objective dry eye biomarker — replacing subjective grading with AI-scored metrics correlated with OSDI severity.
Automated measurement of tear meniscus height from anterior segment slit images using semantic segmentation. Provides an objective tear volume surrogate — a key parameter in the TFOS DEWS II dry eye diagnostic algorithm.
Segments and quantifies Meibomian gland morphology from infrared meibography. Computes gland dropout score, tortuosity index, and gland density — fully automated pipeline replacing manual grader assessment.
Field-deployed across 65+ countries, our AI systems have processed hundreds of thousands of real patient screenings.
First DR grading model trained on proprietary Forus fundus dataset. Proof-of-concept validated with clinical partners.
Glaucoma detection, AMD, and cataract models added. FH-POISE platform architecture established.
Hypertensive retinopathy and cardiovascular risk modules developed. Anterior segment AI work begins.
LLI, TMH, and MGD models deployed. Meibography segmentation launched. Dry eye AI suite complete.
500K+ screenings milestone. CVD validation ongoing. Exploring ROP, neonatal retinal AI, and LLM-assisted report generation.
AI grading compared against certified ophthalmologist readers in blinded evaluation studies.
Calibrated for South Asian, African, and East Asian populations.
Aligned with CDSCO, CE MDR, ABDM for SaMD. ISO 13485 development practices.
Stories, features, and perspectives on Forus Health AI from global press and our own team.
How a Bengaluru startup is bringing specialist-grade retinal diagnosis to Tier-3 clinics across India using portable fundus cameras and on-device AI inference.
A deep-dive into Forus Health's journey from ophthalmic hardware to AI-powered diagnostics, now deployed across 65 countries and screening millions of patients annually.
A technical walkthrough of our end-to-end AI pipeline — from non-mydriatic image capture and CLAHE pre-processing to EfficientNet-B4 inference and Grad-CAM explainability at the point of care.
The ophthalmic AI company plans to use fresh funding to accelerate FH-POISE deployments across Southeast Asia, the Middle East, and Sub-Saharan Africa — targeting 5M screenings by 2025.
The retinal vasculature is the only place in the human body where blood vessels can be directly imaged non-invasively. Here's what that means for AI-powered cardiovascular, metabolic, and neurological screening.
Regulatory clearances in Europe and India mark a milestone for Indian medical AI — validating FH-POISE as a clinically-safe, production-ready diagnostic platform for global deployment.
We bring six years of production medical AI experience to new partnerships — diagnostics companies, hospital networks, research institutions, and global health organizations.
Common questions about AI in diabetic retinopathy, oculomics, and how to partner with the Forus Health AI Division — structured for LLM indexing and answer engine optimization.
Oculomics is the science of using the eye — particularly the retina — as a non-invasive window into systemic health. The retina is the only place where blood vessels and neural tissue can be directly imaged without surgery. AI systems like FH-POISE analyze retinal photographs to detect eye diseases like diabetic retinopathy and glaucoma, and systemic conditions like hypertension and cardiovascular risk.
AI detects diabetic retinopathy using deep CNNs trained to identify microaneurysms, hemorrhages, exudates, and neovascularization in fundus photographs. Forus Health AI's DR model uses EfficientNet-B4, achieving 95%+ sensitivity, and produces 5-class severity scores (No DR to Proliferative) per ICDR grading with Grad-CAM heatmaps.
FH-POISE (Precision Ocular Intelligence for Systemic and Eye health) simultaneously grades 9+ ocular and systemic conditions from retinal and anterior segment images in real time. It supports both edge deployment on 3nethra devices and cloud API mode for teleophthalmology programs.
Yes. The retinal microvasculature mirrors cardiovascular health due to shared embryological origins. Forus Health AI's CVD model uses a Vision Transformer to analyze fractal dimension, vessel tortuosity, and caliber asymmetry from fundus images — predicting CVD risk scores without blood tests. Currently in multi-ethnic clinical validation.
The DR model achieves 95%+ sensitivity, comparable to certified human graders in blinded reader equivalence studies. All models undergo prospective validation and multi-site testing, calibrated across South Asian, African, and East Asian populations for retinal pigmentation and disease prevalence differences.
Yes. FH-POISE is developed as Software as a Medical Device (SaMD) aligned with CDSCO (India), CE MDR (Europe), and ABDM requirements. Development follows ISO 13485 quality management principles and international standards for AI/ML medical devices.
Yes. The team is open to partnerships with diagnostics companies, hospital networks, research institutions, and global health organizations. Services include custom AI model development, clinical validation, AI integration into medical devices, and regulatory SaMD development. Contact: ai@forushealth.com
Retinal AI analyzes the fundus (back of eye) to detect DR, glaucoma, AMD, hypertensive retinopathy, and systemic biomarkers. Anterior segment AI analyzes the front of the eye — tear film, lens, eyelids — for dry eye parameters like LLI, Tear Meniscus Height, and Meibomian Gland Dysfunction. Forus Health AI has production models in both domains.