CLINICAL DECISION SUPPORT · AI

Where Clinical Precision
Meets Artificial Intelligence.

Diagnovate harnesses ensemble deep learning to analyze thyroid ultrasound scans — delivering majority-voted, clinician-ready diagnoses that support and empower physician decision-making.

Request Access Learn More

Designed to assist, not replace. Diagnovate supports clinical judgment — every result is a second opinion, not a final verdict.

97.4%

Confidence Score

1.2s

Inference Time

Models Online

3 / 3 Active

Core Capabilities

AI Models

97.6%

Peak Accuracy

Thyroid Biomarkers

Capabilities

Everything You Need for Thyroid Diagnostics

Ensemble Architecture

Three vision models and three lab models vote independently — reaching consensus before returning any result to the clinician.

Real-Time Inference

Sub-second analysis pipeline from scan upload to majority-voted diagnosis, fully optimized for clinical workflow speed.

Image Enhancement

CLAHE preprocessing sharpens and normalizes thyroid ultrasound scans before feeding them into the vision inference pipeline.

Lab-Based Diagnosis

16 thyroid biomarkers — TSH, T3, TT4, T4U, FTI and more — power an XGBoost + CatBoost + Random Forest ensemble.

Report Generation

Structured clinical reports with confidence scores, majority-vote breakdown, and annotated findings — ready for physician review.

Ensemble Intelligence

Nine Models. Two Pipelines. One Verdict.

Ultrasound Image Pipeline

3 Vision Models

Vision Model

Swin Transformer

Vision transformer capturing long-range spatial dependencies in thyroid ultrasound images via shifted window attention.

96.85%

Accuracy

0.9939

AUC Score

swin_base_patch4_window7_224 — weights from HuggingFace (iimvbii/diagnovate-models)

HIGHEST ACCURACY

Vision Model

DenseNet-121

Dense skip connections preserve fine-grained diagnostic features across 121 layers for superior nodule classification.

97.64%

Accuracy

0.9979

AUC Score

densenet121_thyroid_v2_BEST.pth — 97.64% accuracy, AUC 0.9979

Vision Model

EfficientNet-B4

Compound-scaled efficient architecture for accurate thyroid image classification with minimal computational overhead.

96.85%

Accuracy

0.993

AUC Score

efficientnet_b4 via timm — CPU-optimized for reliable production inference

Lab Results Pipeline

3 ML Models

PRIMARY

Lab Model

XGBoost

Gradient-boosted trees trained on 16 thyroid biomarkers — primary classifier in the lab ensemble.

Handles TSH, T3, TT4, T4U, FTI and 11 additional biomarkers with feature importance ranking.

Lab Model

CatBoost

Categorical boosting algorithm providing robust predictions across diverse thyroid lab panels.

Gradient boosting with ordered boosting and oblivious trees for stable lab predictions.

Lab Model

Random Forest

Ensemble of decision trees delivering reliable majority-voted predictions from thyroid biomarker data.

Handles missing biomarker values gracefully with probabilistic soft voting.

EnhancementAuto-SelectReport

Plus dedicated models for image enhancement, automatic pipeline selection, and structured report generation.

Workflow

From Scan to Verdict in Seconds

Upload Scan

Submit thyroid ultrasound image or lab values via secure API

Preprocessing

CLAHE enhancement and normalization prepare the image for analysis

Ensemble Inference

Three models independently analyze the data and cast votes

Clinical Report

Majority vote with confidence scores delivered in <2 seconds

Upload Scan

Preprocess

Inference × 3

Report

97.4%

Confidence · Majority Vote

Diagnovate

Clinical AI Platform

CLINICAL DECISION SUPPORT · AI

Where Clinical Precision
Meets Artificial Intelligence.

Diagnovate harnesses ensemble deep learning to analyze thyroid ultrasound scans — delivering majority-voted, clinician-ready diagnoses that support and empower physician decision-making.

Request Access Learn More

Designed to assist, not replace. Diagnovate supports clinical judgment — every result is a second opinion, not a final verdict.

97.4%

Confidence Score

1.2s

Inference Time

Models Online

3 / 3 Active

Core Capabilities

AI Models

97.6%

Peak Accuracy

Thyroid Biomarkers

Capabilities

Everything You Need for Thyroid Diagnostics

Ensemble Architecture

Three vision models and three lab models vote independently — reaching consensus before returning any result to the clinician.

Real-Time Inference

Sub-second analysis pipeline from scan upload to majority-voted diagnosis, fully optimized for clinical workflow speed.

Image Enhancement

CLAHE preprocessing sharpens and normalizes thyroid ultrasound scans before feeding them into the vision inference pipeline.

Lab-Based Diagnosis

16 thyroid biomarkers — TSH, T3, TT4, T4U, FTI and more — power an XGBoost + CatBoost + Random Forest ensemble.

Report Generation

Structured clinical reports with confidence scores, majority-vote breakdown, and annotated findings — ready for physician review.

Ensemble Intelligence

Nine Models. Two Pipelines. One Verdict.

Ultrasound Image Pipeline

3 Vision Models

Vision Model

Swin Transformer

Vision transformer capturing long-range spatial dependencies in thyroid ultrasound images via shifted window attention.

96.85%

Accuracy

0.9939

AUC Score

swin_base_patch4_window7_224 — weights from HuggingFace (iimvbii/diagnovate-models)

HIGHEST ACCURACY

Vision Model

DenseNet-121

Dense skip connections preserve fine-grained diagnostic features across 121 layers for superior nodule classification.

97.64%

Accuracy

0.9979

AUC Score

densenet121_thyroid_v2_BEST.pth — 97.64% accuracy, AUC 0.9979

Vision Model

EfficientNet-B4

Compound-scaled efficient architecture for accurate thyroid image classification with minimal computational overhead.

96.85%

Accuracy

0.993

AUC Score

efficientnet_b4 via timm — CPU-optimized for reliable production inference

Lab Results Pipeline

3 ML Models

PRIMARY

Lab Model

XGBoost

Gradient-boosted trees trained on 16 thyroid biomarkers — primary classifier in the lab ensemble.

Handles TSH, T3, TT4, T4U, FTI and 11 additional biomarkers with feature importance ranking.

Lab Model

CatBoost

Categorical boosting algorithm providing robust predictions across diverse thyroid lab panels.

Gradient boosting with ordered boosting and oblivious trees for stable lab predictions.

Lab Model

Random Forest

Ensemble of decision trees delivering reliable majority-voted predictions from thyroid biomarker data.

Handles missing biomarker values gracefully with probabilistic soft voting.

EnhancementAuto-SelectReport

Plus dedicated models for image enhancement, automatic pipeline selection, and structured report generation.

Workflow

From Scan to Verdict in Seconds

Upload Scan

Submit thyroid ultrasound image or lab values via secure API

Preprocessing

CLAHE enhancement and normalization prepare the image for analysis

Ensemble Inference

Three models independently analyze the data and cast votes

Clinical Report

Majority vote with confidence scores delivered in <2 seconds

Upload Scan

Preprocess

Inference × 3

Report

97.4%

Confidence · Majority Vote

Where Clinical PrecisionMeets Artificial Intelligence.

Everything You Need for Thyroid Diagnostics

Nine Models. Two Pipelines. One Verdict.

From Scan to Verdict in Seconds

Where Clinical PrecisionMeets Artificial Intelligence.

Everything You Need for Thyroid Diagnostics

Nine Models. Two Pipelines. One Verdict.

From Scan to Verdict in Seconds

Where Clinical Precision
Meets Artificial Intelligence.

Where Clinical Precision
Meets Artificial Intelligence.