Deep-Learning Algorithm for Early Detection of Intracranial Haemorrhages

Development of an Artificial Intelligence Algorithm for the Detection of Intracranial Haemorrhages

Objective

• This project aims to develop a deep-learning AI algorithm for the early detection of intracranial haemorrhages (ICH) in CT scans, with the goal of improving patient outcomes by enabling timely intervention. The objectives are as follows: Development of a deep-learning algorithm capable of detecting ICH in CT scans., Evaluation and validation of the algorithm's accuracy and performance through data/model testing., Laboratory-scale testing to fine-tune and optimize the algorithm's functionality., Field testing of the AI algorithm in clinical settings to assess its real-world effectiveness.

Description

• RTAs in India result in high fatalities, often due to head injuries., ICH detection delays due to radiologist shortage and reporting time., AI algorithm automates ICH detection in CT scans, aiding timely diagnosis., Focus on improving outcomes, especially in lower-tier medical facilities., AI assists clinicians in quick, accurate ICH identification, potentially saving lives., Trains medical professionals in AI healthcare solutions.

Impact

• Speeds up ICH diagnosis during the critical "golden period"., Enhances survival chances, reduces brain damage severity., Boosts medical personnel efficiency., May reduce healthcare costs., Trains a skilled AI healthcare workforce.