## 3 Results
### Overview of Selected Studies
The second U-Net model was employed to categorize lesions as either melanoma or non-melanoma, utilizing categorical cross-entropy as the loss function and evaluating the performance using the AUC metric. For AUC calculation, the model used the pixel with the highest melanoma probability within the segmentation region. The model demonstrated optimal performance with an AUC score of 83.4% after 1189 epochs of training. The incorporation of a random forest classifier served to enhance the model's output by assigning a weight of 10 to melanoma images, thereby focusing on enhancing specificity and reducing false negatives. This approach, which considered all probability data (not just the highest probability pixel), resulted in an elevated AUC score of 88.5% in the validation set. Moreover, an analysis of the external test cohort revealed that the model attained an AUC value of 0.88, which is equivalent to the result obtained in the internal test cohort. The disparities between the internal and external test results were evident in the analysis of sensitivity and specificity. To validate the algorithm, the main performance measures were sensitivity and specificity, with the AUC additionally used to evaluate the optimal network repetition. Additionally, the algorithm's performance was evaluated in comparison to that of 12 ophthalmologists (resident ophthalmologists, consultant ophthalmologists, and ocular oncologists) and traditional classification systems, including MOLES and TFSOM-UHHD scores. The Kruskal-Wallis and Mann-Whitney U tests were employed to ascertain any statistically significant discrepancies between the human and AI classifications. The DeLong test was used to compare the AUC values between the algorithm systems and the MOLES and TFSOM-UHHD scores. The algorithm demonstrated 100% sensitivity in the detection of melanomas, indicating that it accurately identified all instances of small melanomas without any false negatives. The group of 12 ophthalmologists demonstrated a sensitivity of 85% for the three resident ophthalmologists, 83% for the six consultant ophthalmologists, and 98% for the three ocular oncologists, which was the best among the experts and very close to the algorithm. In general, the algorithm exhibited superior sensitivity (\(p\) = 0.006) in the assessment of melanomas when compared with that of human experts. Nevertheless, its specificity was 74%, indicating that 74% of moles were correctly identified and correctly classified as non-melanoma. In comparison to human experts, resident ophthalmologists demonstrated the lowest specificity at 63%, followed by ocular oncologists at 70%. However, the group of consultant ophthalmologists showed a higher level of specialization at 78%, which was higher than that of the algorithm. In comparison with established methodologies, the algorithm demonstrated an AUC of 0.88, outperforming traditional risk scoring systems such as MOLES and TFSOM-UHHD, which incorporate additional imaging modalities (OCT, US). Statistically significant improvements (\(p\) < 0.001) reinforce the model's reliability, even when using fundus images alone. The algorithm demonstrated a markedly elevated sensitivity compared to that of human evaluators, particularly ophthalmology residents and consultants. However, its specificity exhibited a level of concordance with the outcomes achieved by specialists across experience levels [31]. [PERSON] et al. [32] investigated a self-tracking DL model, RETFound, for differentiating uveal (choroidal) melanoma from nevus. The study involved data from the Liverpool Ocular Oncology Centre, which comprised fundus images of 4255 patients using an Optos UWF camera. The study defined several criteria for the exclusion of poor-quality images, including images that were blurred, those with artifacts, images with significant occlusion of the eye, and images of patients with specific ocular conditions (e.g., vitreous hemorrhage, dense cataracts, tumors more than 50% out of view, etc.).