Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data

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.

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

“…Lung cancer, the leading cause of cancer deaths, accounts for 18.0% of all deaths from cancer worldwide [ 69 ]. Recently, Huang et al [ 48 ] put forward a convenient and non-invasive approach to detecting lung neoplasms. They trained a multi-instance learning model to distinguish benign from malignant pulmonary nodules using scleral images.…”

“…Partial systemic diseases with a well-established pathophysiologic link with the retina have been confirmed by several studies, such as CVD and dementia. In addition to these two cases, novel applications for the detection of hepatobiliary diseases [ 47 ], lung neoplasms [ 48 ], and PCOS [ 49 ] by analyzing subtle variations in external eye images remain incomprehensible, even with the assistance of interpretability analysis. In high-stakes scenarios such as medical care, any suboptimal treatment or misdiagnosis attributable to the unexplainability may generate disastrous clinical, ethical, and legal consequences [ 76 ].…”

“… Classification of the included articles according to the image modalities used and the target systemic diseases [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. …”