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Related Concept Videos

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Mouse Models of Cancer Study02:43

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Related Experiment Video

Updated: Jun 11, 2026

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Deep Learning-Based Diagnostic Model for Ocular Surface Neoplastic Diseases.

Rie Sakata1, Taiyo Shijo1, Yuta Ueno2

  • 1From the Department of Ophthalmology (R.S., T.S., H.M., T.Y.), Tokyo Dental College Ichikawa General Hospital, Chiba, Japan.

American Journal of Ophthalmology
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

A deep learning (DL) model shows high accuracy in diagnosing common ocular surface tumors like pterygium and limbal dermoid. However, its performance on rare malignancies such as melanoma requires further improvement.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Ocular surface tumors encompass a range of benign and malignant conditions.
  • Accurate and timely diagnosis is crucial for effective treatment and patient outcomes.
  • Deep learning (DL) offers potential for automated image analysis in medical diagnostics.

Purpose of the Study:

  • To develop a deep learning (DL) model for diagnosing ocular surface tumors.
  • To evaluate the diagnostic performance of the developed DL model.
  • To compare the DL model's performance against human expert diagnoses.

Main Methods:

  • A DL model based on YOLOv5 architecture was developed.
  • The model was trained on 1,491 ocular surface images across seven disease categories.
  • Performance was validated using 299 external images and compared with assessments from corneal specialists, ophthalmologists, and residents.

Main Results:

  • The DL model achieved a positive predictive value (PPV) of 96.0%, surpassing human experts.
  • High disease-specific PPVs were observed for pterygium (96.7%) and limbal dermoid (93.5%).
  • The model demonstrated high sensitivity and specificity for most common conditions, with lower performance for rare malignancies like melanoma (PPV 38.5%) and MALT lymphoma (PPV 57.9%).

Conclusions:

  • The DL model shows significant promise as a supportive diagnostic tool for common ocular surface tumors.
  • Further refinement is needed to improve diagnostic accuracy for rare and aggressive malignancies.
  • The study highlights the potential of AI in enhancing ophthalmic diagnostic capabilities.