PulmoScan AI: Intelligent Chest X-ray Analysis for Lung Care

Pulmonary diseases remain one of the most underdiagnosed and resource-heavy conditions worldwide. Fast, consistent diagnosis is essential, but traditional image review workflows are slow, manual, and prone to variability between readers. With PulmoScan AI, we deliver a unified platform that combines X-ray classification, risk prediction, and treatment outcome analysis into one intelligent clinical tool.

PulmoScan AI applies deep learning, natural language processing, and statistical modeling to help radiologists and care teams make faster, evidence-based decisions. The system is designed to scale in both hospital environments and remote diagnostics, improving throughput without compromising accuracy.

Project Highlights

• DenseNet model for chest X-ray classification using transfer learning
• Real-world data preparation and class balancing to support robustness
• GradCAM visualizations to show model attention and improve trust
• Decision Trees and Random Forests for patient risk scoring
• T-learners and multivariate models to compare treatment effects
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• BERT-based NLP for interpreting free-text radiology and clinical notes
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• MRI-ready 3D U-Net model for tumor segmentation

From Chest Image to Actionable Risk Profiles

At the core of PulmoScan AI is a pre-trained DenseNet model, fine-tuned on curated chest X-ray datasets for respiratory disease detection. Preprocessing includes image normalization, class rebalancing, and feature extraction to reduce noise and improve learning performance.

Once the images are classified, GradCAM heatmaps highlight regions influencing the model’s decision. These overlays are embedded directly into clinician dashboards, supporting explainability and clinical decision-making.

Alongside classification, structured risk modeling is powered by Decision Trees and Random Forests trained on real patient outcomes. These models extract insight from radiology reports and create an accurate snapshot of disease severity, enabling proactive care planning.

Curious how Festi unifies structured and unstructured data in business tools? See how we connect your systems.

Beyond Diagnosis: Modeling Treatment Outcomes

One of PulmoScan AI’s most valuable capabilities is its treatment effect modeling pipeline. By applying causal inference techniques like T-learners and computing the C-statistic-for-benefit, the system can estimate the expected value of interventions. This provides clinicians and researchers with a data-driven lens to compare the potential outcomes of different treatments or therapies.

These models are benchmarked against traditional statistical methods used in randomized control trials, giving healthcare teams a complementary approach that adapts to real-time, patient-specific data.

Read how Festi supports evidence-based systems in other industries like CPA performance optimization.

NLP for Clinical Notes and Radiology Reports

Medical imaging tells only part of the story. PulmoScan AI includes a BERT-powered natural language processing module trained to extract intent, findings, and observations from radiology and physician notes. This expands the platform’s ability to operate on both image and text data, unifying information streams that are typically siloed in hospital systems.

This same NLP foundation can be extended across departments, providing real-time support in triage, record audits, or AI-powered support chat integrations.

Built for Clinical Scale, Speed, and Trust

PulmoScan AI is designed to meet the needs of real-world hospital systems, not just research labs. It supports high-volume inference, integrates with existing PACS and EHR systems, and offers transparent model validation via:

• AUC-ROC
• Specificity and sensitivity
• Positive and negative predictive value (PPV/NPV)
• Confidence intervals with per-class breakdowns

Data scientists and clinical users can customize models, retrain using local datasets, and audit decision paths, all within a modular and maintainable system.

Why PulmoScan AI Delivers Value

PulmoScan AI helps reduce diagnostic bottlenecks, supports more confident decisions, and drives more personalized care for patients with respiratory conditions. From automated image review to structured outcome prediction, the system delivers end-to-end support without adding workflow burden.