## 3 Results
### Overview of Selected Studies
A retrospective multicenter analysis was used, including 2870 nevi cases from Wills Eye Hospital and Mayo Clinic. Various imaging techniques were used, including fundus photography, FAF, SD-OCT, and B-scan US. Fundus images were acquired using a Zeiss camera at Wills Eye Hospital and a Topcon camera at Mayo Clinic. Several tree-based ML algorithms were tested, including XGBoost, LGBM, Random Forest, and Extra Tree models, with SAINTS using XGBoost as the kernel. The [PERSON] cohort was split into an 80% training set and 20% test set, with 5-fold cross-validation, while the Mayo cohort served as an external validation set. The metrics used to assess model performance were the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). The ML models XGBoost (SAINTS), LGBM, Random Forest, and Extra Tree were optimized for AUROC. The SAINTS model achieved the highest performance, with an AUROC of 0.864 in the test cohort and 0.931 in external validation. Other models showed slightly lower AUROC values: LGBM (0.831/0.815), Random Forest (0.812/0.866), and Extra Tree (0.826/0.915) in the test and external validation tion cohorts, respectively. Similarly, SAINTS outperformed the other models in AUPRC, achieving 0.244 in the test cohort and 0.533 in external validation. The AUPRC values for the other models were LGBM (0.171/0.277), Random Forest (0.122/0.418), and Extra Tree (0.119/0.511) in the test and external validation cohorts, respectively. Key prognostic factors identified by SHAP (Shapley additive explanations) included tumor thickness, largest basal diameter, tumor shape, distance from the optic nerve, and amount of subretinal fluid. These findings highlight the accuracy and generalizability of ML in predicting choroidal nevus transformation, potentially aiding in clinical decision-making [30]. [PERSON] and colleagues [31] developed and validated a DL algorithm for the differentiation of SCM from nevi based on the analysis of wide- and standard-range fundus photographs. The bottom images were sourced from several institutions, with a particular focus on those pertaining to SCM. A total of 802 images from 688 patients at St. Erik Eye Hospital in Stockholm were included in the study, comprising both wide-field (200\({}^{\circ}\)) (WF-FP) and standard-field (45\({}^{\circ}\)) (SF-FP) retinal photographs. The images were collected retrospectively, with a particular focus on small melanotic choroidal lesions and were obtained from 2010 onwards, with diagnoses confirmed by subspecialist ophthalmic oncologists. In the case of lesions diagnosed as nevi, a minimum of five years of follow-up was required to confirm their benign nature. Images of insufficient quality or those with partially masked lesions were excluded, resulting in 802 valid images. The dataset was divided into three groups: a training group (495 images), a validation group (168 images), and a test group for internal and external cohorts (86 and 53 images), respectively. During the image preprocessing stage, fundus images were resized to a resolution of 1024 \(\times\) 1536 pixels, normalized for brightness, and scaled to a pixel range of (0, 1). A U-Net architectural model comprising three downsampling layers and eight basic filters, with the ReLU (rectified linear unit) as the activation function, was employed for the analysis of nevi and melanomas. The model demonstrated enhanced interpretability and a heightened focus on specific regions of the lesion. Two distinct U-Net networks were incorporated in Explorer's Explipipe training pipeline: one for lesion segmentation and another for melanoma classification. The first model was designed to identify the damaged region, utilizing the categorical cross-entropy as the loss function and evaluation metric. At the output of the model, a center point was calculated for the center of a bounded box around the lesion (488 \(\times\) 488 pixels), which then served as input to the second model.