## 4 AI for the Study of Inflammatory Diseases of the Orbit
### Application of AI in Inflammatory Diseases of the Orbit  
In the publications we have included, AI is employed in four main areas, all of which are aimed at better understanding the pathophysiology and improving the management of NSOI. First, this technology seems useful in identifying transcriptional biomarkers for differential diagnosis. In this regard, the study conducted by [PERSON] et al. (2024) [33] developed models to identify immune biomarkers, focusing on regulatory genes such as GPR145, which is involved in macrophage activation, and constructing a diagnostic signature based on 15 genes selected by LASSO regression and SVM-RFE. The analysis showed the selective overexpression of GPR146 in NSOI, compared with healthy tissues with a higher AUC of 0.90, suggesting potential as a discriminative biomarker between inflammatory and noninflammatory conditions of the orbit.  
Further exploration in this area includes the study of immune-mediated metabolism, particularly the study of glutamine and inflammation. The study conducted by [PERSON] et al. (2024) [34] explored the role of glutamine metabolism in orbital inflammation, and the researchers used automated feature selection (LASSO and SVM-RFE) on a set of 79 glutamine-related genes and succeeded in identifying a subset of 14 genes predictive of NSOI. From this study, through the use of AI, a direct correlation between glutamine gene activity and the infiltration of immune cells, such as macrophages and lymphocytes, was shown, supporting the hypothesis of an immune-dependent metabolism as a pathophysiological role.  
Another aspect is immune and orbital microenvironment analysis. In two studies, [PERSON] et al. (2024) [33; 34] applied the CIBERSORT algorithm, combined with the ESTIMATE package, to deconstruct the immune infiltration in NSOI biopsies. This analysis allowed them to computationally estimate the proportions of CD8+ T cells, M1 and M2 macrophages, dendritic cells, and natural killer cells, providing noninvasive immunologic mapping of orbital inflammation.  
The study conducted by [PERSON] et al. (2022) [35] performed unsupervised clustering of the transcriptome of patients with orbital inflammation and succeeded in identifying three distinct molecular subtypes, including one with high inflammatory activation, one that was immunosuppressive, and that was one mixed. This study used a combination of machine learning and functional analysis (KEGG and GSVA), allowing us to hypothesize different therapeutic responses depending on the molecular phenotype.  
The following table (Table 5) shows the studies found in the literature regarding the application of artificial intelligence models in the differential diagnosis of orbital neoplastic lesions. Information such as the models used, the input data processed by the algorithms, the evaluation method, and the results obtained from individual studies are also given.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Objective of** & **AI Model Used** & **Data Input** & **Output** & **Validation** & **Advantages** \\  & **the Study** & & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. (2024) [33]} & Identifying & LASSO, & mRNA microarray & & & Noninvasive \\  & immune & SVM-RFE, & (Affymetrix & Signature with & Cross- & diagnosis, high \\  & biomarkers for & GSVA, GSEA, & GPL570). Dataset & 15 genes hub & validation + & AUC (>0.90), \\  & NSOI & CIBERSORT & GSE58331 (train), & (GPR146) & external testing & identification of \\  & & GSE105149 (test) & & & immune targets \\ \hline \hline \end{tabular}
\end{table}
Table 5: Applications of AI in Inflammatory Diseases of the Orbit.