[Machine learning technologies in CT-based diagnostics and classification of intracranial hemorrhages]

Area of Science:

• Radiology and Medical Imaging
• Computer Science and Artificial Intelligence
• Neurology

Background:

• Intracranial hemorrhage diagnosis relies heavily on computed tomography (CT) imaging.
• Machine learning (ML) and deep learning (DL) offer potential advancements in medical image analysis.
• The integration of artificial intelligence (AI) in clinical practice for neurological conditions is rapidly evolving.

Approach:

• A systematic review of 21 original articles published between 2015 and 2022 was conducted.
• The review focused on studies involving machine learning, deep learning, and artificial intelligence for CT-based intracranial hemorrhage detection.
• Keywords used included: "intracranial hemorrhage", "machine learning", "deep learning", and "artificial intelligence".

Key Points:

• AI algorithms demonstrate potential in identifying intracranial hemorrhages on CT scans.
• Technical characteristics of datasets significantly influence the effectiveness of AI diagnostic tools.
• Understanding the impact of data set variations is crucial for reliable AI implementation.

Conclusions:

• Machine learning technologies are increasingly effective in the CT-based diagnosis of intracranial hemorrhages.
• Further research into dataset characteristics is essential for optimizing AI algorithm performance in clinical settings.
• AI holds significant promise for enhancing the speed and accuracy of intracranial hemorrhage diagnosis.