## 3 Results
### Overview of Selected Studies  
[PERSON] et al. [25] developed a ML model for the distinction between small choroidal melanoma (SCM) and CN. The training data came from a retrospective cohort of 123 patients in the USA with small choroidal melanocytic tumors, ranging from 5.0 to 16.0 mm in basal diameter and 1.0 to 2.5 mm in height. A total of 61 patients were classified as having SCM based on either documented growth or pathological confirmation. The remaining 62 lesions were categorized as nevi due to their stable nature. A distinct dataset comprising 240 patients from an alternative clinic was employed for external validation purposes, with 11 cases of confirmed melanoma based on observed growth. A clinical diagnosis of SCM was assigned to each patient via a standard examination comprising a slit lamp and fundus examination. A comprehensive fundus drawing was created to provide a visual representation of the lesion's full extent, which was supplemented by color fundus photographs. The baseline variables were preselected and included age at diagnosis, gender, laterality of the eye, presence of symptoms at presentation, initial optimally corrected visual acuity, distances from the optic nerve and fovea, largest basal diameter, lesion thickness, presence of subretinal fluid (SRF), orange pigment, drusen, and retinal pigment epithelium (RPE) atrophy. In model development, the LASSO logistic regression method was employed to identify the most predictive variables for classifying melanoma by reducing the coefficients of the least significant variables to zero. The most significant features identified were SRF, tumor height, proximity to the optic disc, and orange pigment. Bootstrap resampling with 10-fold cross-validation was conducted for internal validation purposes in order to assess the robustness of the model and to avoid overfitting. The generalizability of the model was tested on an external dataset, with both discrimination AUC and calibration evaluated. Consequently, a web-based calculator was created, providing a probability estimate for melanoma, which may assist with clinical decision-making. The analysis demonstrated that an optic disc distance of \(\geq\)3 mm in comparison to \(<\)3 mm, in conjunction with the presence of drusen, was associated with a reduced risk of melanoma. Conversely, male gender, increased lesion height, SRF, and orange pigmentation were identified as factors that were linked to an elevated risk of melanoma. The model demonstrated high accuracy in distinguishing melanoma from nevus, with an AUC of 0.880 in the training data and an optimism-corrected AUC of 0.849. The external validation demonstrated an AUC of 0.861, confirming the model's capacity for robust discrimination. However, the calibration exhibited slight inaccuracy, with the model exhibiting an overestimation of melanoma risk across the prediction range [25]. [PERSON] et al. [26] evaluated the efficacy of automated image segmentation in identifying pigmented choroidal lesions (PCLs) at the pixel level. To achieve this, three distinct DL networks were employed: MD-GRU (multi-dimensional gated recurrent unit), V-Net and nnU-Net. The data were derived from 71 individuals in Switzerland, and a total of 121 OCT volumes were analyzed: 100 normal and 21 PCLs. Prior to OCT imaging, all subjects underwent a slit-lamp clinical examination and a fundus examination. The OCT data were recorded using a Triton DRI SS-OCT device, which produced volume scans with a resolution of 6 mm \(\times\) 6 mm \(\times\) 2.6 mm (256 B-scans, 512 \(\times\) 992 pixels). Two independent human graders manually annotated OCT images to identify PCLs using the open-source FIJI imaging software (v2.1). The repeatability of the annotation was evaluated using Dice coefficients to assess consistency within and between graders. The MD-GRU, V-Net, and nnU-Net models were trained on both 2D and 3D variations and their performance was evaluated using accuracy, Dice coefficient, recall, precision, and the Hausdorff distance.