Kaapana is an open source toolkit for state of the art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. The name Kaapana comes from the Hawaiian word kaʻāpana, meaning "distributor" or "part".

ACR Assist is a clinical decision support framework designed to provide structured clinical guidance to radiologists in a manner that allows this content to be incorporated naturally into the radiology workflow.

RadCLIP is a foundation model for radiologic imaging that leverages a Vision–Language Pre-training (VLP) framework to align 2D/3D radiologic images with their textual descriptions, improving diagnostic accuracy and efficiency in clinical workflows.

A Python-based engine for processing radiology reports using the Qwen3 model with sglang for efficient batch inference. Includes quality control file generation and performance evaluation tools for comprehensive validation workflows with debug mode for faster iteration.

Radiology Right Click v1.0 is a radiologist's quantitative friend, designed to improve quantitative radiology and streamline workflow. This AutoHotkey script provides a suite of tools accessible via right-click, offering saved references, AI prompts and calculations targeted to various radiological measurements and assessments.

Full-Stack Medical Center App A healthcare management system supporting five roles (Admin, Sub-Admin, Radiologist, Prescriber, Patient) with seamless prescription and radiology workflow integration. Includes Twilio SMS for real-time notifications to enhance communication and operational efficiency.

A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow

TraumaCB is a LLM-powered diagnostic tool designed to enhance trauma radiology workflows by integrating advanced Retrieval-Augmented Generation (RAG) with OpenAI's GPT-4 Turbo.

Using Deep Learning and Computer Vision for automated Pathology Classification in radiology. This PyTorch project applies a CNN with Transfer Learning to showcase proof-of-concept for an AI-assisted diagnostic workflow.

Core system for the electronic management of imaging departments. The major functions of this RIS include patient scheduling, resource management, examination performance tracking, reporting and results distribution

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Agentic workflow for radiology using medgeamma1.5

A Project To Simulate The Imaging Workflow of a Hospital's Radiology Department

I converted 1,000 ROCO v2 radiology captions into JSONL format for AI research workflows

A suite of AI-powered tools to streamline radiology workflows—automating patient intake, contouring, and treatment planning.

Medical app providing lung nodule management tools based on Fleischner and Lung-RADS guidelines with calculator workflows for radiology.

Physician-gated critical imaging copilot - 8 AI agents coordinate emergency triage, outpatient follow-up, and radiology workflows using MedGemma. Built for MedGemma Impact Challenge.

This repo contains a set of tools and recommendation for managing AI projects in radiology, focusing on data management, segmentation, task assignment, and workflow creation.

DemHC is a web based platform that aims to assist radiologists/doctors in many stages of radiological workflow by performing x-ray classification of human lungs for potential abnormality utilizing CNN model deployed in Django.

This is a toolkit for using Mondrian conformal prediction in radiological deep learning workflows. It is developed and maintained by the Mayo Clinic AI Lab under the supervision of Bradley J. Erickson, M.D., Ph.D.

This is a toolkit for using Mondrian conformal prediction in radiological deep learning workflows. It is developed and maintained by the Mayo Clinic AI Lab under the supervision of Bradley J. Erickson, M.D., Ph.D.

AI-assisted chest X-ray abnormality detection system using YOLOv11-L to classify and localize pathologies with bounding boxes. Built on VinDr-CXR with a full ML pipeline, cloud-native infrastructure, distributed training, and production-grade serving, monitoring, and evaluation for radiology workflows.

Osterio Annex - X-Ray Tracker System Osterio Annex is a comprehensive X-ray tracking system designed to manage and streamline radiology workflows in hospitals and clinics. This system ensures efficient patient queue management, real-time machine tracking, and automated patient allocation to X-ray machines.

Quantifying Hippocampus Volume for Alzheimer's Progression Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder that results in impaired neuronal (brain cell) function and eventually, cell death. AD is the most common cause of dementia. Clinically, it is characterized by memory loss, inability to learn new material, loss of language function, and other manifestations. For patients exhibiting early symptoms, quantifying disease progression over time can help direct therapy and disease management. A radiological study via MRI exam is currently one of the most advanced methods to quantify the disease. In particular, the measurement of hippocampal volume has proven useful to diagnose and track progression in several brain disorders, most notably in AD. Studies have shown a reduced volume of the hippocampus in patients with AD. The hippocampus is a critical structure of the human brain (and the brain of other vertebrates) that plays important roles in the consolidation of information from short-term memory to long-term memory. In other words, the hippocampus is thought to be responsible for memory and learning (that's why we are all here, after all!) Hippocampus Source: Life Science Databases (LSDB). Hippocampus. Images are from Anatomography maintained by Life Science Databases (LSDB). (2010). CC-BY-SA 2.1jp. Link Humans have two hippocampi, one in each hemisphere of the brain. They are located in the medial temporal lobe of the brain. Fun fact - the word "hippocampus" is roughly translated from Greek as "horselike" because of the similarity to a seahorse observed by one of the first anatomists to illustrate the structure, but you can also see the comparison in the following image. Seahorse & Hippocampus Source: Seress, Laszlo. Laszlo Seress' preparation of a human hippocampus alongside a sea horse. (1980). CC-BY-SA 1.0. Link According to Nobis et al., 2019, the volume of hippocampus varies in a population, depending on various parameters, within certain boundaries, and it is possible to identify a "normal" range taking into account age, sex and brain hemisphere. You can see this in the image below where the right hippocampal volume of women across ages 52 - 71 is shown. Nomogram - Female, Right Hippocampus Volume, Corrected for Head Size Source: Nobis, L., Manohar, S.G., Smith, S.M., Alfaro-Almagro, F., Jenkinson, M., Mackay, C.E., Husain, M. Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank. Neuroimage: Clinical, 23(2019), pp. 2213-1582. There is one problem with measuring the volume of the hippocampus using MRI scans, though - namely, the process tends to be quite tedious since every slice of the 3D volume needs to be analyzed, and the shape of the structure needs to be traced. The fact that the hippocampus has a non-uniform shape only makes it more challenging. Do you think you could spot the hippocampi in this axial slice below? Axial slice of an MRI image of the brain As you might have guessed by now, we are going to build a piece of AI software that could help clinicians perform this task faster and more consistently. You have seen throughout the course that a large part of AI development effort is taken up by curating the dataset and proving clinical efficacy. In this project, we will focus on the technical aspects of building a segmentation model and integrating it into the clinician's workflow, leaving the dataset curation and model validation questions largely outside the scope of this project.

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This repository builds a prototype workflow leveraging Agentic AI to enhance the productivity of a radiologist.

Example code for workflow optimization microservices

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Ignyt Radiology Workflow Management Software

A game of radiology workflows.