## 3 AI for the Study of Orbital Tumors
### Applications of AI in Orbital Tumors  
Several applications of AI models for the study of orbital tumors are reported in the literature. This technology is proving useful for tumor classification. In fact, one of the major areas of application is the distinction between benign and malignant tumors from CT or MRI images. For example, the study conducted by [PERSON] et al. (2023) [23] developed a deep learning system, based on U-Net for segmentation and ResNet-34 for classification, for processing non-contrast CT scans. The system achieved an accuracy of 87% and an AUC of 0.95, demonstrating performance compatible with that of experienced clinicians. In addition, the study conducted by [PERSON] et al. (2024) [24] used multiparametricMRI scans (DWI, DCE, and IVIM) combined with a machine learning, random forest model for lesion classification as input data, obtaining an AUC of 0.90. Another crucial application in the diagnostic pathway is differential diagnosis. In fact, the study conducted by [PERSON] et al. (2022) [PERSON] et al. (2022) applied a machine learning model for the analysis of non-contrast CT images to distinguish orbital cavern hemangiomas (OCVM) from other similar lesions. Using 13 ML models and six feature selection methods, they obtained an AUC of 0.94 and an accuracy of 88%, further demonstrating that CT scans without a contrast medium, when analyzed by AI algorithms, can provide useful diagnostic data, especially in cases where a contrast medium cannot be used, such as in cases of patient allergy. Further application is illustrated by the study conducted by [PERSON] et al. (2022) [PERSON] et al. (2022), where a CNN model, based on VGG16, is presented to predict orbital invasion by naso-sinus tumors, demonstrating 92% accuracy and improved performance of general radiologists from 45-49% to 94-100% when assisted by AI. In addition to lesion classification, there are systems that can perform automatic tumor segmentation. [PERSON] et al. (2024) [PERSON] et al. (2024) introduced a semi-supervised network, MSCINet, designed for orbital tumor segmentation on CT scans. The model uses a self-learning strategy with uncertainty filters and a multiscale coherence constraint, demonstrating performance aligned with supervised methods while using a limited number of annotated images. Finally, another application found in the literature is noninvasive diagnosis on superficial clinical images. The study conducted by [PERSON] et al. (2024) [PERSON] et al. (2024) explored the use of this technology to identify ocular surface squamous neoplasia (OSSN) early and noninvasively. Clinical images of the ocular surface, combined with cytology and histological examinations, were processed by AI models to augment and improve diagnosis and guide early topical treatment, avoiding the need for invasive biopsies. The following table (Table 3) shows the studies found in the literature regarding the application of artificial intelligence models in the diagnosis and study of orbital neoplastic lesions. Information such as the models used, the input data processed by the algorithms, the evaluation method, the results, and the main benefits obtained from the individual studies are also given. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Objective of** \\ **the Study** \\ \end{tabular}  & **AI Model Used** & **Data Input** & **Results** & **Validation** & **Advantages** \\ \hline \multirow{4}{*}{ \begin{tabular}{c} [PERSON] et al. \\ (2022) [PERSON] et al. \\ (2022) [PERSON] et al. \\ (2022) [PERSON] et al. \\ (2022) [PERSON] et al. \\ (2022) [PERSON] et al. \\ (2022) [PERSON] et al. \\ (2022) \\ \end{tabular}  } & \begin{tabular}{c} ML (SVM, SGD, \\ RF, XGBoost) \\ with the selection \\ of characteristics \\ \end{tabular}  & \begin{tabular}{c} A total 335 total cases: axial \\ CT images without contrast in \\ DICOM format, typical size: \\ S12 \(\times\) 512 px, \\ anisotropic voxels \\ \end{tabular}  & \begin{tabular}{c} \multirow{2}{*}{