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Classification of Epithelial Tissues: Overview

Classification of Epithelial Tissues: Overview

Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
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Updated: Sep 11, 2025

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Multimodal artificial intelligence for subepithelial lesion classification and characterization: a multicenter

Jiao Li^1,2, Xiaojuan Jing³, Qin Zhang⁴

¹Department of Gastroenterology, The Second Affiliated Hospital of Chongqing Medical University, Linjiang Road 76#, Chongqing, Yuzhong District, China.

BMC Medical Informatics and Decision Making

|August 15, 2025

Summary

A new AI model, ECMAI-WME, integrates endoscopy and ultrasound to accurately classify gastrointestinal subepithelial lesions. This deep learning tool significantly outperforms human endoscopists in diagnosis and treatment decisions.

Keywords:

Artificial intelligence Endoscopic ultrasound Gastrointestinal stromal tumors Gastrointestinal subepithelial lesions Neuroendocrine tumors

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Gastroenterology
Artificial Intelligence
Medical Imaging

Background:

Gastrointestinal subepithelial lesions (SELs) pose diagnostic challenges, especially distinguishing malignant from benign types.
Misdiagnosis of SELs can lead to inappropriate interventions or delayed treatment.
Accurate characterization of SELs is crucial for effective patient management.

Purpose of the Study:

To develop and evaluate ECMAI-WME, a parallel fusion deep learning model integrating white light endoscopy (WLE) and microprobe endoscopic ultrasonography (EUS).
To improve the classification and characterization of gastrointestinal subepithelial lesions.
To enhance diagnostic accuracy and support clinical decision-making in SEL management.

Main Methods:

Developed serial and parallel fusion AI models using data from 523 SELs across four hospitals.
Designated the superior performing model as ECMAI-WME (a parallel fusion model).
Validated ECMAI-WME on external (n=88) and multicenter (n=274) cohorts, comparing its performance against endoscopists.

Main Results:

ECMAI-WME significantly outperformed endoscopists in diagnostic accuracy (96.35% vs. 63.87-86.13%) and treatment decision accuracy (96.35% vs. 78.47-86.13%).
Achieved high accuracy in multiclass SEL classification and characterization (94.81% mean accuracy).
Demonstrated robust performance and generalizability across validation cohorts and subgroup analyses.

Conclusions:

The ECMAI-WME model shows excellent diagnostic performance and robustness for multiclass SEL classification and characterization.
Supports potential for real-time deployment to improve diagnostic consistency.
Aids in guiding clinical decision-making for gastrointestinal subepithelial lesions.