Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data

Qin Huang; Wenqi Lv; Zhanping Zhou; Shuting Tan; Xue Lin; Zihao Bo; Rongxin Fu; Xiangyu Jin; Yuchen Guo; Hongwu Wang; Feng Xu; Guoliang Huang

doi:10.3390/diagnostics13040648

Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data

Diagnostics (Basel). 2023 Feb 9;13(4):648. doi: 10.3390/diagnostics13040648.

Authors

Qin Huang¹, Wenqi Lv¹, Zhanping Zhou², Shuting Tan³, Xue Lin¹, Zihao Bo², Rongxin Fu¹, Xiangyu Jin¹, Yuchen Guo⁴, Hongwu Wang^{5

6}, Feng Xu², Guoliang Huang^{1

7}

Affiliations

¹ Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
² BNRist and School of Software, Tsinghua University, Beijing 100084, China.
³ Graduate School, Adamson University, Manila 1000, Philippines.
⁴ Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China.
⁵ Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.
⁶ Emergency General Hospital, Beijing 100000, China.
⁷ National Engineering Research Center for Beijing Biochip Technology, Beijing 102206, China.

Abstract

Lung cancer remains the most commonly diagnosed cancer and the leading cause of death from cancer. Recent research shows that the human eye can provide useful information about one's health status, but few studies have revealed that the eye's features are associated with the risk of cancer. The aims of this paper are to explore the association between scleral features and lung neoplasms and develop a non-invasive artificial intelligence (AI) method for detecting lung neoplasms based on scleral images. A novel instrument was specially developed to take the reflection-free scleral images. Then, various algorithms and different strategies were applied to find the most effective deep learning algorithm. Ultimately, the detection method based on scleral images and the multi-instance learning (MIL) model was developed to predict benign or malignant lung neoplasms. From March 2017 to January 2019, 3923 subjects were recruited for the experiment. Using the pathological diagnosis of bronchoscopy as the gold standard, 95 participants were enrolled to take scleral image screens, and 950 scleral images were fed to AI analysis. Our non-invasive AI method had an AUC of 0.897 ± 0.041(95% CI), a sensitivity of 0.836 ± 0.048 (95% CI), and a specificity of 0.828 ± 0.095 (95% CI) for distinguishing between benign and malignant lung nodules. This study suggested that scleral features such as blood vessels may be associated with lung cancer, and the non-invasive AI method based on scleral images can assist in lung neoplasm detection. This technique may hold promise for evaluating the risk of lung cancer in an asymptomatic population in areas with a shortage of medical resources and as a cost-effective adjunctive tool for LDCT screening at hospitals.

Keywords: artificial intelligence (AI); lung neoplasms; multi-instance learning model; sclera image.

Grants and funding

This research was funded by the National Key Research and Development Program of China, grant number 2018YFA0704000; the Sichuan Science and Technology Program, grant number 2021YFQ0060; the National Natural Science Foundation of China, grant number 61927819, 81827808 and 62105177; Vanke Special Fund for Public Health and Health Discipline Development, Tsinghua University, grant number 2022Z82WKJ002; the Tsinghua University Spring Breeze Fund, grant number 2020Z99CFG011; the Beijing Lab Foundation, and the Tsinghua Autonomous Research Foundation, grant number 20194180031, 20201080058, 20201080510; and Tsinghua Laboratory Innovation Fund, grant number 100020019.