LibreHealth

Mentors: Student: DevAnuragT

NeoRoo currently talks to DHIS2 through raw HTTP calls with no offline support, so if a nurse loses connectivity while entering baby data, that data is just gone. I'm proposing to integrate the d2 Android SDK as the primary sync engine and build a Flutter abstraction layer on top of it that works across both Android and iOS. The main deliverables are: a Pigeon-based platform bridge to d2 (Android) and DHIS2 REST API (iOS), an offline-first sync queue using Drift/SQLite with automatic retry, a server-wins conflict resolution system with a review log, real-time sync status indicators in the UI, background sync with battery optimization, push notifications for server-side changes, and a testing + monitoring toolkit. By the end, healthcare workers should be able to use NeoRoo reliably regardless of connectivity.

Mentors: Student: Sujal Tripathi

LibreHealth Toolkit has no visual way for non-developers to build healthcare forms without writing code. I will build a drag-and-drop form designer using vanilla Web Components and Lit, consisting of a form-designer-canvas, component-palette, properties-panel, and FhirDataBinder. The designer will let users visually assemble FHIR- bound healthcare forms, export/import JSON schemas, and preview forms in real time , all without requiring the end user to write a single line of code.

Mentors: Student: Kishan Singh 07

LibreHealth EHR manages sensitive patient data but currently has no automated security testing or vulnerability detection integrated into its development workflow. This means security issues like SQL injection, cross-site scripting, and broken access control can go undetected during code reviews. This project builds a four-layer automated security assessment pipeline integrated directly into the CI/CD workflow. The pipeline consists of a static analysis stage using PHPStan, Enlightn, and Composer Audit to catch code-level vulnerabilities; a dynamic testing stage using OWASP ZAP to simulate real-world attacks against a running EHR instance; a custom CVSS scoring engine that adjusts risk scores based on PHI sensitivity of the affected data; and a security gate that automatically blocks merge requests when high-severity vulnerabilities are detected. The key deliverables are a Meaningful Use workflow security test suite covering 7 clinical workflows, a fully configured GitLab CI pipeline, an automated security gate with PHI-aware scoring, and per-build HTML, JSON, and CSV security reports for both humans and machines.

Mentors: Student: Tejas S A

The radiology Artifact Detection aims to create a system that identifies image artifacts on user selected regions of interest. till now the radiologists are manually finding the artifacts . in this project i aim to create a system that helps the radiologists in this process . The scanning reports will contain 100's of scripts and the traditional segmentation models works on pre trained data and breakdowns when it encounters a rare or a completely new data. The solution which i have planned is using an MedSAM model as it classify the scanning reports based on the visual signatures of the user's selection. A 2.5D volumetric tracking in this once an artifact is identified on Slice N, the algorithm prioritizes searching the exact spatial coordinates on Slices N-1 and N+1 which reduces the requirement of computation power. planning of using the human in the loop method in which out of 10 if the 8 predictions are correct and 2 are wrong if the user deletes 2 wrong predictions then the model learns that these two are wrong. i planed of using DICOM for datamanagement. The structure: Frontend -> model -> datainput -> MedSAM model -> loss calculation and scoring -> datamanagement.

Mentors: Student: Akash Srivastava

The Essential Care for Every Baby (ECEB) mobile application is designed to offer clinical decision support to healthcare personnel who provide essential care interventions for newborns. The project aims to improve the user experience, accuracy, and reliability of the app by revamping its user interface, improving the rule-based classification algorithm, resolving syncing issues on local storage and the DHIS2 rule engine, enhancing the app's functionality, and developing user manuals and training materials. The project will be implemented in four phases using Agile Development Methodology. The first phase will revamp the application based on UX changes, the second phase will improve the rule-based classification algorithm, the third phase will address DHIS2 rule engine and syncing issues, and the fourth phase will focus on documentation. The enhancements proposed in the design include making the application more accessible and user-friendly with more visual cues, legible fonts, and features that increase functionality. The goal is to ensure that healthcare workers in low and middle-income regions have essential knowledge, skills, and competencies to deliver life-saving care to newborns.