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Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

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Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data.

Qin Huang1, Wenqi Lv1, Zhanping Zhou2

  • 1Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.

Diagnostics (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a non-invasive artificial intelligence (AI) method using eye scleral images to detect lung cancer. This AI tool shows promise for early lung neoplasm detection, especially in underserved areas.

Keywords:
artificial intelligence (AI)lung neoplasmsmulti-instance learning modelsclera image

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Area of Science:

  • Ophthalmology
  • Oncology
  • Artificial Intelligence

Background:

  • Lung cancer is a leading cause of cancer death.
  • The human eye may offer insights into health, but its link to cancer risk is underexplored.
  • Scleral features' association with lung neoplasms requires further investigation.

Purpose of the Study:

  • To explore the association between scleral features and lung neoplasms.
  • To develop a non-invasive artificial intelligence (AI) method for lung neoplasm detection using scleral images.

Main Methods:

  • A novel instrument captured reflection-free scleral images.
  • Deep learning algorithms and multi-instance learning (MIL) were applied for analysis.
  • AI model performance was evaluated against pathological diagnosis from bronchoscopy.

Main Results:

  • The AI method achieved an AUC of 0.897 ± 0.041 for distinguishing benign from malignant lung nodules.
  • Sensitivity was 0.836 ± 0.048 and specificity was 0.828 ± 0.095.
  • Scleral features, like blood vessels, may correlate with lung cancer risk.

Conclusions:

  • Scleral features may be associated with lung cancer.
  • A non-invasive AI method using scleral images can aid in lung neoplasm detection.
  • This technique offers potential for early lung cancer risk evaluation in resource-limited settings and as a complementary tool for LDCT screening.