Artificial intelligence to detect malignant eyelid tumors from photographic images

Abstract

Malignant eyelid tumors can invade adjacent structures and pose a threat to vision and even life. Early identification of malignant eyelid tumors is crucial to avoiding substantial morbidity and mortality. However, differentiating malignant eyelid tumors from benign ones can be challenging for primary care physicians and even some ophthalmologists. Here, based on 1,417 photographic images from 851 patients across three hospitals, we developed an artificial intelligence system using a faster region-based convolutional neural network and deep learning classification networks to automatically locate eyelid tumors and then distinguish between malignant and benign eyelid tumors. The system performed well in both internal and external test sets (AUCs ranged from 0.899 to 0.955). The performance of the system is comparable to that of a senior ophthalmologist, indicating that this system has the potential to be used at the screening stage for promoting the early detection and treatment of malignant eyelid tumors.

Noninvasive identification of Benign and malignant eyelid tumors using clinical images via deep learning system

“…Recently, Adamopoulos et al [48] and Li et al [49] also developed AI system to detect eyelid tumors. The details of comparison with these studies have been listed in Table 4.…”

“…In addition, this study did not provide some important evaluation metrics including sensitivity, specificity, and AUC. Li [49] et al also developed DL model to identify malignant eyelid tumors from benign ones with bigger sample size. Before identifying the characteristics of the tumors, they trained model to locate the tumor first with an average precision of 76.2%, which meant about a quarter of mass was wrongly located.…”

Facilitating deep learning through preprocessing of optical coherence tomography images

“…Deep learning allows computer algorithms the freedom to select features without extensive human input, thereby transcending historical roles. In ophthalmology, deep learning has been applied across almost all subspecialties including retina, glaucoma, neuro-ophthalmology, and oculoplastics [2][3][4][5][6]. Specifically, retinal optical coherence tomography images have been tested extensively via deep learning to identify diagnoses, clinical features, and anatomical retinal layer segmentation [7,8].…”

A cascade eye diseases screening system with interpretability and expandability in ultra-wide field fundus images: A multicentre diagnostic accuracy study

“…Several studies tried to visualize the algorithms using heatmaps. 18 , 19 , 20 But it was difficult to explain whether the highlight region was a new finding or a model error. 21 , 22 And highlighted regions were not precise enough to locate small abnormalities in retina.…”