The "Intelligent Eyelid Tumor Screening" WeChat application was established based on the developed models (Fig. 5 and the Supplementary Movie 1). Users can easily complete the identification process by following the given instructions (Fig. 5a). The standard preparation guidelines for higher-quality image input are shown in Fig. 5b. Users can look back at the previous identification records for long-term follow-up work (Fig. 5c) and attain a basic understanding of common eyelid tumors (Fig. 5d). Users can also register online for further offline checkups (Fig. 5e). The inner account allows professionals to review and record medical data (Fig. 5f).
We developed and validated a smartphone-based application that provides a holistic and quantitative technique for detecting and identifying eyelid tumors in patients. Our system achieved an AUC of 0.917 on external datasets and proved reliable and stable results under different settings when used by patients and professionals. We evaluated this system in real-world clinical settings. Intelligent Eyelid Tumor Screening captured photographs of patients who visited our clinic and then automatically detected and categorized them into normal, benign, and malignant eyes, and the predicted probabilities were given. No further information, including the chief complaints, basic information, or tumor descriptions of the patients, was considered during tumor screening and identification.
Eyelid tumors are common but are often not given sufficient care because they do not impact vision; most patients seek medication for cosmetic reasons. Although benign tumors account for most eyelid tumors, malignant tumors have considerable potential for morbidity and mortality. BCC is the most common malignant eyelid tumor and is usually observed in the elderly population with excessive sun exposure. Even though it has a low fatality rate, BCC can be associated with significant morbidity and costs. SCC is reported to be a common malignancy of the ocular surface, particularly in areas with high ultraviolet light exposure and skin damage; it is frequently over-diagnosed by pathologists and histologically confused with other benign entities. SGC is a rare but aggressive neoplasm, and its five-year mortality rate can reach 30%. The early detection and identification of malignant tumors can potentially increase the probability of timely treatments, further improving patient prognoses.
In our study, 33.54% of the eyelid tumors were malignant. However, malignant tumors accounted for approximately 12% to 15% of all tumors identified in previous epidemiological investigations conducted in both eastern and western countries. This difference may have been due to the sample size employed in our study, and hard-to-treat patients were more likely to visit our hospital. As in previous studies, BCC, SCC and SGC were the top three malignant tumors in our dataset, with nevus, xanthelasma and seborrheic keratosis being the top three benign tumors.
Differentiating malignant and benign tumors with the naked eye can be challenging for junior ophthalmologists and nonspecialist physicians because of the relative rarity of each subtype, the overlapping clinical features between different subtypes and a lack of ophthalmologic training, leading to minimal specialized clinical experience.
AI systems have the advantages of high accuracy and efficiency when capturing information in ways that the human brain cannot. Several automatic and semiautomatic approaches have been developed to detect eyelid tumors. Li et al. also developed an AI system for distinguishing malignant eyelid tumors from benign tumors in multicenter clinics. They trained a tumor localization model with an average precision of 76.2%, which meant that approximately one-quarter of masses were incorrectly located. Lee et al. developed two models for classifying hand-cropped images into two or three categories without an ocular localization model. Therefore, the human ophthalmologists in our study were assigned to precisely delineate the tumors during the development stage of the AI model so that our model could achieve better performance, and this approach might be more appropriate for decision-making in clinical settings.
Lord et al. first proposed the novel use of smartphones in ophthalmology. The detailed use of smartphone-based applications in ophthalmology was described later by various researchers, and such applications have more recently been employed in various clinical practices. Previous studies utilized photographs to detect ocular and visual abnormalities. In our study, we analyzed more than 1200 ocular photographs and accurately distinguished malignant eyelid tumors from benign tumors and normal eyes. Moreover, to utilize our application in various scenarios, we used different blur, brightness and smartphone platforms to evaluate the stability of our system.
This study has several limitations. First, we focused on only a single static image type; different lighting conditions, camera settings, and shooting scenarios and a lack of three-dimensional vision may have influenced the feature extraction process, even when we evaluated the stability of our system under different conditions. However, our system introduced the suggested shooting distance and lighting conditions and allowed users to continuously import images until a qualified photo was acquired, thereby improving the success rate of the identification process. Second, the system provided results based only on the given images, without additional clinical information assisting the analysis procedure. Ting et al. suggested that the inclusion of clinical information may improve the accuracy of model detection and identification. Third, our recruited dataset may not have fully represented the epidemiological population; even though we achieved effective identification in clinical applications, large-scale screening is still needed in the future to validate our system in a real-world setting. In addition, premalignant lesions were not included. Fourth, the use of AI with imaging modalities raises several ethical concerns. AI algorithms require large volumes of patient data for training, testing and validation purposes. Our system collects facial information from users, which raises questions about data access and privacy protection for patients. We aim to add a virtual mask for privacy protection in future research. AI systems can inherit biases that are present in training data. With the use of AI in medical imaging identification tasks, determining the responsibility for errors and misdiagnoses in application scenarios can be challenging. When a false positive occurs, individuals may experience unnecessary stress, anxiety, and concern about their diagnosis. Conversely, a false negative can result in missing opportunities for early treatment or intervention, potentially worsening their condition.
Given the complexity inherent in distinguishing eyelid tumors, the concept of accurately identifying eyelid tumors is attractive. At present, our model provides outcomes consisting of the probabilities of benign and malignant tumors, as well as guidance for tumor introduction and further medical treatment recommendations. In future work, we could focus on expanding our approach to three-dimensional graphs and further extending the subtype identification capabilities of our system to improve our model and identify more subtypes.
This study introduced the first smartphone-based portable eyelid tumor detection application for WeChat: "Intelligent Eyelid Tumor Screening". Smartphone-based applications constitute an emerging research area with respect to designing small-sized, low-power, high-quality and affordable systems that can perform eyelid tumor screening and automated detection. Based on the obtained results, eyelid tumors could be identified within 2 seconds. At this stage, the recognition sensitivity can reach 88% with a specificity of 95%, and the recognition sensitivity can be further improved after continuously learning from uploaded data. This smartphone-based eyelid tumor detection application has the potential to be used by health care professionals, patients and caregivers for detecting and monitoring eyelid tumors, providing an alternative to frequent hospital visits and invasive biological processes.
