# Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans

arXiv: https://arxiv.org/abs/1803.05854 · HTML: https://mlsanity.com/paper/1803.05854/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 24 of 24)

- [3D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generation](https://mlsanity.com/paper/2412.13059/) (2024)
- [BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset](https://mlsanity.com/paper/2308.11298/) (2023)
- [MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders](https://mlsanity.com/paper/2502.14753/) (2025)
- [Deep Generative Models for 3D Medical Image Synthesis](https://mlsanity.com/paper/2410.17664/) (2024)
- [Cultural Heritage 3D Reconstruction with Diffusion Networks](https://mlsanity.com/paper/2410.10927/) (2024)
- [Weakly supervised deep learning-based intracranial hemorrhage localization](https://mlsanity.com/paper/2105.00781/) (2021)
- [Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis](https://mlsanity.com/paper/2407.08948/) (2024)
- [MEPNet: Medical Entity-balanced Prompting Network for Brain CT Report Generation](https://mlsanity.com/paper/2503.17784/) (2025)
- [Recovering medical images from CT film photos](https://mlsanity.com/paper/2203.05567/) (2022)
- [One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging](https://mlsanity.com/paper/2212.03630/) (2022)
- [PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage](https://mlsanity.com/paper/2308.10521/) (2023)
- [Interpretable 3D Multi-Modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis](https://mlsanity.com/paper/2309.12572/) (2023)
- [See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning](https://mlsanity.com/paper/2409.19676/) (2024)
- [Unsupervised Acute Intracranial Hemorrhage Segmentation with Mixture Models](https://mlsanity.com/paper/2105.05891/) (2021)
- [Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI using Noisy Student-based Training](https://mlsanity.com/paper/2111.05959/) (2021)
- [Monocular Depth Decomposition of Semi-Transparent Volume Renderings](https://mlsanity.com/paper/2206.13282/) (2022)
- [Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices](https://mlsanity.com/paper/2208.03403/) (2022)
- [Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows](https://mlsanity.com/paper/2502.11638/) (2025)
- [A Semantic Segmentation Network Based Real-Time Computer-Aided Diagnosis System for Hydatidiform Mole Hydrops Lesion Recognition in Microscopic View](https://mlsanity.com/paper/2204.04949/) (2022)
- [An Effective Transformer-based Solution for RSNA Intracranial Hemorrhage Detection Competition](https://mlsanity.com/paper/2205.07556/) (2022)
- [Using ScrutinAI for Visual Inspection of DNN Performance in a Medical Use Case](https://mlsanity.com/paper/2308.01220/) (2023)
- [Federated Voxel Scene Graph for Intracranial Hemorrhage](https://mlsanity.com/paper/2411.00578/) (2024)
- [AI-Driven Radiology Report Generation for Traumatic Brain Injuries](https://mlsanity.com/paper/2510.08498/) (2025)
- [CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking](https://mlsanity.com/paper/2601.02521/) (2026)

## Abstract (arXiv)

Importance: Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. Objective: To develop and validate a set of deep learning algorithms for automated detection of following key findings from non-contrast head CT scans: intracranial hemorrhage (ICH) and its types, intraparenchymal (IPH), intraventricular (IVH), subdural (SDH), extradural (EDH) and subarachnoid (SAH) hemorrhages, calvarial fractures, midline shift and mass effect. Design and Settings: We retrospectively collected a dataset containing 313,318 head CT scans along with their clinical reports from various centers. A part of this dataset (Qure25k dataset) was used to validate and the rest to develop algorithms. Additionally, a dataset (CQ500 dataset) was collected from different centers in two batches B1 & B2 to clinically validate the algorithms. Main Outcomes and Measures: Original clinical radiology report and consensus of three independent radiologists were considered as gold standard for Qure25k and CQ500 datasets respectively. Area under receiver operating characteristics curve (AUC) for each finding was primarily used to evaluate the algorithms. Results: Qure25k dataset contained 21,095 scans (mean age 43.31; 42.87% female) while batches B1 and B2 of CQ500 dataset consisted of 214 (mean age 43.40; 43.92% female) and 277 (mean age 51.70; 30.31% female) scans respectively. On Qure25k dataset, the algorithms achieved AUCs of 0.9194, 0.8977, 0.9559, 0.9161, 0.9288 and 0.9044 for detecting ICH, IPH, IVH, SDH, EDH and SAH respectively. AUCs for the same on CQ500 dataset were 0.9419, 0.9544, 0.9310, 0.9521, 0.9731 and 0.9574 respectively. For detecting calvarial fractures, midline shift and mass effect, AUCs on Qure25k dataset were 0.9244, 0.9276 and 0.8583 respectively, while AUCs on CQ500 dataset were 0.9624, 0.9697 and 0.9216 respectively.