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Updated: Aug 19, 2025

Symmetric Bihemispheric Postmortem Brain Cutting to Study Healthy and Pathological Brain Conditions in Humans

Symmetric Bihemispheric Postmortem Brain Cutting to Study Healthy and Pathological Brain Conditions in Humans

Published on: December 18, 2016

Artificial intelligence techniques for neuropathological diagnostics and research.

Islam Alzoubi¹, Guoqing Bao¹, Yuqi Zheng²

¹School of Computer Science, The University of Sydney, Sydney, New South Wales, Australia.

Neuropathology : Official Journal of the Japanese Society of Neuropathology

|November 29, 2022

Summary

This summary is machine-generated.

Artificial intelligence (AI) is emerging in digital neuropathology to analyze complex brain disease data. Advanced machine learning techniques, including deep learning, show promise in overcoming diagnostic challenges and enhancing neuropathological practice.

Keywords:

artificial intelligence (AI)brain research graph neural network (GNN)neural network (NN)neuropathological diagnostics

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

Area of Science:

Neuropathology
Artificial Intelligence
Digital Pathology

Background:

Artificial intelligence (AI) research has historical roots in neurophysiology.
The application of AI in digital neuropathology is a rapidly advancing field.
Human brain diseases present complex functional and structural abnormalities crucial for diagnosis.

Purpose of the Study:

To discuss the role of AI and machine learning (ML) in digital neuropathology.
To introduce the neuropathological AI tool PathoFusion.
To explore challenges and future potential of AI in analyzing whole-slide images (WSIs) for brain disease diagnosis.

Main Methods:

Discussion of traditional ML and deep learning techniques for WSI analysis.
Introduction of PathoFusion, a novel AI for neuropathology.
Exploration of data analysis challenges in digital neuropathology.

Main Results:

AI, particularly ML and deep learning, offers significant potential for analyzing complex neuropathological data.
Whole-slide image (WSI) analysis using AI can aid in diagnosing brain diseases.
PathoFusion represents a step towards AI-driven neuropathological diagnosis.

Conclusions:

AI and WSI analysis are poised to revolutionize digital neuropathology.
Addressing current challenges is key to realizing AI's full potential in diagnosing brain diseases.
AI tools like PathoFusion can help overcome shortages in expert neuropathologists and data processing capacity.