What does this research mean for the field?

The PathInteract suite accurately extracts pathologist interaction signals, such as cursor movements and viewport actions, directly from standard screen recordings without requiring specialized tracking equipment. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to design and test a video analysis suite for analyzing how pathologists interact with pathology slides.

July 30, 2026Scientific ReportsOpen Access

PathInteract: a video analysis suite for capturing and analyzing pathology slide reviewing interactions

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Authors

JJJun Jiang QGQiangqiang Gu XZXin Zhou

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Overview

Proof-of-concept suite analyzes pathology slide reviewing interactions, enhancing AI tool design implications.

Key Points

The aim is to design and test a video analysis suite for analyzing how pathologists interact with pathology slides.
Developed PathInteract for extracting interaction signals from recorded slide-review videos.
Utilized deep learning and computer vision for cursor movement and viewport action detection.
Analyzed educational YouTube videos alongside QuPath-recorded diagnostic sessions.
Pathologists showed distinct interaction patterns based on experience and tissue type.
Cursor tracking and viewport detection demonstrated high accuracy compared to ground truth.
Speech analysis revealed differences in focus on cell-level details across diseases.

Cite This Study

Jiang et al. (2026) studied this question.

synapsesocial.com/papers/6a6af54160e2b924d3ea129b https://doi.org/10.1038/s41598-026-61758-2

View Full Paper

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July 30, 2026Scientific ReportsOpen Access

PathInteract: a video analysis suite for capturing and analyzing pathology slide reviewing interactions

View Full Paper

Ask AI

Bookmark

Authors

JJJun Jiang QGQiangqiang Gu XZXin Zhou

Discussion

Member takes

Overview

Proof-of-concept suite analyzes pathology slide reviewing interactions, enhancing AI tool design implications.

Key Points

The aim is to design and test a video analysis suite for analyzing how pathologists interact with pathology slides.
Developed PathInteract for extracting interaction signals from recorded slide-review videos.
Utilized deep learning and computer vision for cursor movement and viewport action detection.
Analyzed educational YouTube videos alongside QuPath-recorded diagnostic sessions.
Pathologists showed distinct interaction patterns based on experience and tissue type.
Cursor tracking and viewport detection demonstrated high accuracy compared to ground truth.
Speech analysis revealed differences in focus on cell-level details across diseases.

Cite This Study

Jiang et al. (2026) studied this question.

synapsesocial.com/papers/6a6af54160e2b924d3ea129b https://doi.org/10.1038/s41598-026-61758-2

View Full Paper

Ask AI

Bookmark