TANUH

Neurological disorders are complex, data-intensive, and deeply influenced by population-specific factors such as anatomy, ageing patterns, and disease prevalence. However, most existing medical AI models are trained on Western datasets, making them poorly suited for Indian populations.

India presents unique neurological characteristics—from differences in brain structure and ageing trajectories to higher prevalence of conditions like neuro-tuberculosis and nutritional deficiencies. These gaps result in reduced accuracy in diagnosis, limited clinical relevance, and inequitable healthcare outcomes.

This creates a critical need for a sovereign, India-specific medical foundation model that can understand and operate within the country’s clinical and demographic realities.

Our Solution

IndiNeuroFM is a Unified Multimodal Medical Foundation Model (UMMFM) designed specifically for the Indian brain.

It integrates imaging, clinical data, and biomarkers to enable accurate diagnosis, prognosis, and research across neurological conditions.

The platform acts as a multimodal bridge, connecting different forms of medical data to generate clinically meaningful insights at scale.

IndiNeuroFM is a Unified Multimodal Medical Foundation Model (UMMFM) designed specifically for the Indian brain.

It integrates imaging, clinical data, and biomarkers to enable accurate diagnosis, prognosis, and research across neurological conditions.

The platform acts as a multimodal bridge, connecting different forms of medical data to generate clinically meaningful insights at scale.

IndiNeuroFM is a Unified Multimodal Medical Foundation Model (UMMFM) designed specifically for the Indian brain.

It integrates imaging, clinical data, and biomarkers to enable accurate diagnosis, prognosis, and research across neurological conditions.

The platform acts as a multimodal bridge, connecting different forms of medical data to generate clinically meaningful insights at scale.

Solution 1: Multimodal Brain Intelligence Engine

A foundation model that enables cross-modal understanding and generation across medical data types.

Capabilities:

Convert brain scans (MRI/CT) into structured clinical reports
Generate cross-modality imaging (MRI ↔ CT)
Create synthetic brain imaging from clinical prompts
Learn unified representations across imaging, text, and metadata

A foundation model that enables cross-modal understanding and generation across medical data types.

Capabilities:

Convert brain scans (MRI/CT) into structured clinical reports
Generate cross-modality imaging (MRI ↔ CT)
Create synthetic brain imaging from clinical prompts
Learn unified representations across imaging, text, and metadata

A foundation model that enables cross-modal understanding and generation across medical data types.

Capabilities:

Convert brain scans (MRI/CT) into structured clinical reports
Generate cross-modality imaging (MRI ↔ CT)
Create synthetic brain imaging from clinical prompts
Learn unified representations across imaging, text, and metadata

Goals

Improve diagnostic accuracy in neurological imaging

Reduce dependency on manual interpretation

Enable scalable clinical decision support

Key Features of the Solution:

Multimodal AI combining imaging, text, and metadata

3D volumetric understanding of brain scans

Structured “Neuro-Tuple” data architecture

Foundation model trained on India-specific datasets

Solution 2: Cognitive Impairment Staging & Prognostics

Uses multimodal biomarkers to classify and predict stages of cognitive impairment.

Capabilities:

Classify Mild Cognitive Impairment (MCI) stages
Predict progression trajectories of cognitive decline
Integrate clinical, cognitive, imaging, and biochemical data

Uses multimodal biomarkers to classify and predict stages of cognitive impairment.

Capabilities:

Classify Mild Cognitive Impairment (MCI) stages
Predict progression trajectories of cognitive decline
Integrate clinical, cognitive, imaging, and biochemical data

Uses multimodal biomarkers to classify and predict stages of cognitive impairment.

Capabilities:

Classify Mild Cognitive Impairment (MCI) stages
Predict progression trajectories of cognitive decline
Integrate clinical, cognitive, imaging, and biochemical data

Goals

Enable early detection of cognitive decline

Support proactive intervention and care planning

Differentiate normal ageing from pathological conditions

Key Features of the Solution:

Large-scale multimodal dataset (clinical + imaging + biomarkers)

Population-specific modelling across rural and urban cohorts

Explainable AI using SHAP for transparent decision-making

Clinically grounded biomarker prioritization

Impact & Vision

Current Impact:

Building India’s first sovereign neurological foundation model

Training on 70,000+ brain imaging volumes across MRI and CT modalities

Leveraging 20,000+ multimodal clinical, cognitive, and biomarker data points

Advancing state-of-the-art performance in:

Imaging segmentation and analysis
Clinical report generation
Cognitive impairment classification (>85% target accuracy)

Enabling explainable AI for transparent and clinically interpretable decisions

Powering multi-institutional collaboration across hospitals, diagnostics, and research centres in India

Addressing population-specific gaps in diagnosis for conditions like neuro-infections, nutritional deficiencies, and atypical ageing patterns

Building India’s first sovereign neurological foundation model

Training on 70,000+ brain imaging volumes across MRI and CT modalities

Leveraging 20,000+ multimodal clinical, cognitive, and biomarker data points

Advancing state-of-the-art performance in:

Imaging segmentation and analysis
Clinical report generation
Cognitive impairment classification (>85% target accuracy)

Enabling explainable AI for transparent and clinically interpretable decisions

Powering multi-institutional collaboration across hospitals, diagnostics, and research centres in India

Addressing population-specific gaps in diagnosis for conditions like neuro-infections, nutritional deficiencies, and atypical ageing patterns

Building India’s first sovereign neurological foundation model

Training on 70,000+ brain imaging volumes across MRI and CT modalities

Leveraging 20,000+ multimodal clinical, cognitive, and biomarker data points

Advancing state-of-the-art performance in:

Imaging segmentation and analysis
Clinical report generation
Cognitive impairment classification (>85% target accuracy)

Enabling explainable AI for transparent and clinically interpretable decisions

Powering multi-institutional collaboration across hospitals, diagnostics, and research centres in India

Addressing population-specific gaps in diagnosis for conditions like neuro-infections, nutritional deficiencies, and atypical ageing patterns

Future Vision:

To establish a sovereign, India-first AI infrastructure for neurological healthcare

To enable accurate, equitable, and population-specific diagnosis across diverse Indian contexts

To build a unified multimodal intelligence layer connecting imaging, clinical data, and biomarkers

To support early detection and proactive care for neurological and cognitive disorders

To create a scalable foundation for future medical AI innovation in India and the Global South

To drive clinically grounded, transparent, and ethical AI systems for real-world deployment