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Correction: Tsuneki et al. Deep Learning-Based Screening of Urothelial Carcinoma in Whole Slide Images of Liquid-Based Cytology Urine Specimens. <i>Cancers</i> 2023, <i>15</i>, 226.

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High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples

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Automated Assessment of Ki-67 Labeling Index Using Cell-Level Detection and Classification in Whole-Slide Images.

Masayuki Tsuneki¹, Meng Li¹, Fahdi Kanavati¹

¹Medmain Research, Medmain Inc., 2-4-5-104, Akasaka, Chuo-ku, Fukuoka 810-0042, Japan.

Diagnostics (Basel, Switzerland)

|March 14, 2026

Summary

An artificial intelligence (AI) system automates Ki-67 labeling index (LI) assessment, improving tumor proliferation marker reproducibility. This AI tool shows performance comparable to expert pathologists, aiding routine histopathology.

Keywords:

AI Ki-67 classification detection labeling index pathology

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

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

Published on: July 11, 2025

Area of Science:

Oncology
Pathology
Artificial Intelligence

Background:

Ki-67 labeling index (LI) is crucial for assessing tumor proliferation.
Manual Ki-67 LI assessment is time-consuming and prone to significant inter-observer variability.
Automated methods are needed to enhance reproducibility in clinical practice.

Purpose of the Study:

To evaluate an AI-based system for automated, cell-level Ki-67 LI assessment.
To compare the AI system's performance against expert pathologists.
To determine the clinical relevance of AI in Ki-67 LI evaluation.

Main Methods:

Developed an AI system using a convolutional neural network for cell-level nuclear classification (Ki-67-positive/negative).
Utilized a pre-existing cell detection model for nucleus identification.
Trained and applied the AI classifier to histopathology cases independently assessed by three pathologists.

Main Results:

The AI cell classification achieved 98% AUC on a large test set.
The AI system demonstrated concordance with expert pathologists similar to human inter-observer variability.
AI-driven Ki-67 LI assessment showed high accuracy across various proliferation levels.

Conclusions:

Cell-level automated Ki-67 assessment holds significant potential for improving diagnostic consistency.
The AI system can serve as a reproducible decision-support tool in routine histopathology.
AI-powered Ki-67 LI analysis offers a reliable alternative to manual scoring.