Artificial Intelligence in Decoding Ocular Enigmas: A Literature Review of Choroidal Nevus and Choroidal Melanoma Assessment  
[PERSON]  
11 st Department of Ophthalmology, G. Gennimatas Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; [EMAIL_ADDRESS] (K.-E.K. ); [EMAIL_ADDRESS] (E.M.); [EMAIL_ADDRESS] (P.P. ); [EMAIL_ADDRESS] (I.G.) 1  
[PERSON]  
11 st Department of Ophthalmology, G. Gennimatas Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; [EMAIL_ADDRESS] (K.-E.K. ); [EMAIL_ADDRESS] (E.M.); [EMAIL_ADDRESS] (P.P. ); [EMAIL_ADDRESS] (I.G.) [PERSON]  
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[PERSON]  
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Tries authors contributed equally to this work.2  
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[PERSON]  
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Tries authors contributed equally to this work.2  
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Tries authors contributed equally to this work. Several scoring systems have been developed to aid in CN classification, including the Collaborative Ocular Melanoma Study (COMS) and the American Joint Committee on Cancer (AJCC) staging system. In addition, various clinical tools help specialists recognize and manage suspicious lesions, such as the MOLES scoring system, and TFSOM-UHHD criteria (Table 1) [4; 5; 6]. Uveal melanomas (UM) arise from melanocytes originating from the neural crest. These cells are typically distinguished by their spindle-shaped nuclei and pigmented cytoplasm [7]. UM represents the most prevalent form of primary eye cancer in adults, with an estimated global incidence of over 7000 cases annually [8]. The development of UM has been linked to variations in eye color, as demonstrated by two single nucleotide polymorphisms (SNPs) (rs12913832 in HERC2 and rs12203592 in IRF4) [9]. From a genetic perspective, melanoma is a multifaceted disease involving alterations in genes that regulate key signaling pathways, including:  
1. Cell proliferation (NRAS, BRAF, NF1). 2. Growth and metabolism (STK11, PTEN, KIT). 3. Reproductive capacity (TERT). 4. Cell cycle regulation (CDKN2A). 5. Resistance to apoptosis (TP53). These genetic alterations are critical for the pathogenesis and progression of melanoma, affecting cell growth, proliferation, and survival [10]. The diagnosis of a choroidal nevus is usually made during an ophthalmological examination. Imaging techniques such as fundus photography, optical coherence tomography (OCT), and fluorescein angiography (FA) are used to visualize the nevus and can provide detailed imaging for monitoring over time [1]. Conventional ultrasound (US) is a valuable diagnostic tool for detecting larger tumors. Recently, high-frequency ultrasound biomicroscopy (UBM) has been recognized as an effective method for detecting and monitoring small ciliary body melanomas (less than 4 mm) that might otherwise be missed by other conventional techniques [5]. Posterior UM has a strong propensity for metastasis, with a relative survival rate of 60% at 15 years after diagnosis [11]. Since distinguishing CN from CM is challenging with traditional imaging, artificial intelligence (AI) offers a valuable tool to improve diagnosis and risk assessment. By leveraging machine learning (ML) or deep learning (DL) algorithms, AI models can analyze complex imaging features that may not be visible to the human eye, helping with earlier and more accurate detection. AI has rapidly advanced in medical imaging, particularly in ophthalmology. Early applications of AI in eye diseases included the CASNET-based glaucoma consultation program in 1976, which demonstrated the potential for ML in clinical settings [12]. Today, ML techniques, especially those employing supervised models trained on fundus photographs, hold considerable promise in distinguishing CN from melanoma and assessing malignancy