Applications of Artificial Intelligence in the Study and Diagnosis of Orbital Pathologies: State of the Art and Future Directions
Source abstract
Abstract
Background: Diseases of the orbit, particularly tumors, present complex diagnostic challenges due to the variability of clinical manifestations and the delicate anatomical region. Artificial intelligence could be the appropriate tool to assist the physician in clinical decision making. This review aims to investigate the potential applications of such technology for the analysis and management of orbital pathologies, investigate the advantages and limitations, and offer suggestions for future research. Methods: Selected studies were included for the conduct of this review by adopting the PRISMA guidelines. Results: AI algorithms represent an innovative tool for orbital diagnostics with potential applications in screening, surgery planning, and follow-up. Despite this, limitations and challenges persist that need to be addressed with future research. Conclusions: Clinical decision systems are proving to be a useful tool for the management of orbital pathology. For full integration into clinical reality, some actions are needed, including the standardization of protocols and the implementation of multicenter studies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Facial Trauma and Fracture Management · Dental Radiography and Imaging · Medical Imaging and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Clinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
Division of Neurosurgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Piazza Spedali Civili 1, 25123 Brescia, Italy
Academic Neurosurgery, Department of Neurosciences, University of Padova, 35121 Padova, Italy
Division of Neurosurgery, Department of Neurological Sciences, Ospedale del Mare, 80147 Napoli, Italy
Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
Mediterranean Foundation “G.B. Morgagni”, 95125 Catania, Italy
Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy
Department of Medicine, Surgery and Health Sciences, University of Trieste, 34127 Trieste, Italy
References (37)
Show source-provided references
1. Differential Diagnosis of Lateral Orbital Masses10.1016/0149-936X(81)90050-3 ↗
2. Diagnosis of Orbital Mass Lesions: Clinical, Radiological, and Pathological Recommendations10.1016/j.survophthal.2019.06.006 ↗
3. Contrast-Enhanced Ultrasound in the Diagnosis of Orbital Space-Occupying Lesions10.1016/j.crad.2017.03.026 ↗
4. An Analysis of 2,480 Space-Occupying Lesions of the Orbit From 1976 to 201110.1097/IOP.0b013e31827a7622 ↗
5. Nerad, J.A. (2001). Oculoplastic Surgery: The Requisites in Ophthalmology, Mosby.
6. Characterization of Orbital Masses by Multiparametric MRI10.1016/j.ejrad.2015.11.041 ↗
7. Orbital Trauma10.1055/s-0039-1685477 ↗
8. Maxillofacial Fractures: Midface and Internal Orbit—Part II: Principles and Surgical Treatment10.1055/s-0035-1563693 ↗
9. Pearls of Orbital Trauma Management10.1055/s-0030-1269769 ↗
10. Pediatric Orbital Floor Fractures: Nausea/Vomiting as Signs of Entrapment10.1016/S0194-59980300487-X ↗
11. Medial Orbital Wall Fractures and the Transcaruncular Approach10.1097/SCS.0b013e31824dba84 ↗
12. Demographics and Treatment Options for Orbital Roof Fractures10.1067/moe.2002.120975 ↗
13. Epidemiology and Management of Orbital Fractures: Curr10.1097/01.icu.0000136113.56288.87 ↗
14. Kim, S., Koh, H.K., Lee, H., and Shin, H.J. (2025). Deep-Learning Method for the Diagnosis and Classification of Orbital Blowout Fracture Based on Computed Tomography. J. Oral Maxillofac. Surg., S0278-2391(25)00243-5.10.1016/j.joms.2025.04.010 ↗
15. Automatic Detection of Midfacial Fractures in Facial Bone CT Images Using Deep Learning-Based Object Detection Models10.1016/j.jormas.2024.101914 ↗
16. Automatic Identification and Segmentation of Orbital Blowout Fractures Based on Artificial Intelligence10.1167/tvst.12.4.7 ↗
17. Automatic Orbital Segmentation Using Deep Learning-Based 2D U-Net and Accuracy Evaluation: A Retrospective Study10.1016/j.jcms.2023.09.003 ↗
18. Fully Automated Segmentation of Globes for Volume Quantification in CT Images of Orbits Using Deep Learning10.3174/ajnr.A6538 ↗
19. Yu, F., Liu, C., Zhong, C., Zeng, W., Chen, J., Liu, W., Guo, J., and Tang, W. (2025). 3D Deep Learning for Virtual Orbital Defect Reconstruction: A Precise and Automated Approach. J. Craniofacial Surg.10.1097/SCS.0000000000011143 ↗
20. Intelligent Surgical Planning for Automatic Reconstruction of Orbital Blowout Fracture Using a Prior Adversarial Generative Network10.1016/j.media.2024.103332 ↗
21. Assessing the Accuracy of Artificial Intelligence in the Diagnosis and Management of Orbital Fractures: Is This the Future of Surgical Decision-Making?10.1016/j.jpra.2024.09.014 ↗
22. Deep Convolutional Neural Networks for Automatic Detection of Orbital Blowout Fractures10.1097/SCS.0000000000006069 ↗
23. Shao, J., Zhu, J., Jin, K., Guan, X., Jian, T., Xue, Y., Wang, C., Xu, X., Sun, F., and Si, K. (2023). End-to-End Deep-Learning-Based Diagnosis of Benign and Malignant Orbital Tumors on Computed Tomography Images. J. Pers. Med., 13.10.3390/jpm13020204 ↗
24. Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI10.1097/RLI.0000000000001076 ↗
25. Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool10.1097/SCS.0000000000008446 ↗
26. Utility of the Deep Learning Technique for the Diagnosis of Orbital Invasion on CT in Patients with a Nasal or Sinonasal Tumor10.1186/s40644-022-00492-0 ↗
27. Multi-Scale Consistent Self-Training Network for Semi-Supervised Orbital Tumor Segmentation10.1002/mp.16945 ↗
28. Novel Automated Non-Invasive Detection of Ocular Surface Squamous Neoplasia Using Artificial Intelligence10.5662/wjm.v14.i2.92267 ↗
29. MRI-Based Radiomics Nomogram for Distinguishing Solitary Fibrous Tumor from Schwannoma in the Orbit: A Two-Center Study10.1007/s00330-023-10031-5 ↗
30. Shao, Y., Chen, Y., Chen, S., and Wei, R. (2023). Radiomics Analysis of T1WI and T2WI Magnetic Resonance Images to Differentiate between IgG4-Related Ophthalmic Disease and Orbital MALT Lymphoma. BMC Ophthalmol., 23.10.1186/s12886-023-03036-7 ↗
31. Artificial Intelligence-Based Differential Diagnosis of Orbital MALT Lymphoma and IgG4 Related Ophthalmic Disease Using Hematoxylin-Eosin Images10.1007/s00417-024-06501-1 ↗
32. Deep Learning-Based Intratumoral and Peritumoral Features for Differentiating Ocular Adnexal Lymphoma and Idiopathic Orbital Inflammation10.1007/s00330-024-11275-5 ↗
33. Wu, Z., Li, L., Xu, T., Hu, Y., Peng, X., Zhang, Z., Yao, X., and Peng, Q. (2024). Elucidating the Multifaceted Roles of GPR146 in Non-Specific Orbital Inflammation: A Concerted Analytical Approach through the Prisms of Bioinformatics and Machine Learning. Front. Med., 11.10.3389/fmed.2024.1309510 ↗
34. Wu, Z., Li, N., Gao, Y., Cao, L., Yao, X., and Peng, Q. (2024). Glutamine Metabolism-Related Genes and Immunotherapy in Nonspecific Orbital Inflammation Were Validated Using Bioinformatics and Machine Learning. BMC Genom., 25.10.1186/s12864-023-09946-6 ↗
35. Differentiating Intradiploic Orbital Dermoid and Epidermoid Cysts Utilizing Clinical Features and Machine Learning10.4103/ijo.IJO_52_22 ↗
36. Automated Delineation of Orbital Abscess Depicted on CT Scan Using Deep Learning10.1002/mp.14907 ↗
37. Wu, Z., Fang, C., Hu, Y., Peng, X., Zhang, Z., Yao, X., and Peng, Q. (2024). Bioinformatic Validation and Machine Learning-Based Exploration of Purine Metabolism-Related Gene Signatures in the Context of Immunotherapeutic Strategies for Nonspecific Orbital Inflammation. Front. Immunol., 15.10.3389/fimmu.2024.1318316 ↗