Aging of Chinese bony orbit: automatic calculation based on UNet++ and connected component analysis

Application of artificial intelligence in oculoplastics

Oculoplastics is a subspecialty of ophthalmology/dermatology concerned with eyelid, orbital, and lacrimal diseases. Artificial intelligence (AI), with its powerful ability to analyze large data sets, has dramatically benefited oculoplastics. The cutting-edge AI technology is widely applied to extract ocular parameters and to use these results for further assessment, such as screening and diagnosis of blepharoptosis and predicting the progression of thyroid eye disease. AI also assists in treatment procedures, such as surgical strategy planning in blepharoptosis. High efficiency and high reliability are the most apparent advantages of AI, with promising prospects. The possibilities of AI in oculoplastics may lie in three-dimensional modeling technology and image generation. We retrospectively summarize AI applications involving eyelid, orbital, and lacrimal diseases in oculoplastics, and we also examine the strengths and weaknesses of AI technology in oculoplastics.

Insights into Orbital Symmetry: A Comprehensive Retrospective Study of 372 Computed Tomography Scans

Background: The operation planning and production of individualized implants with the help of AI-based software after orbital fractures have become increasingly important in recent years. This retrospective study aimed to investigate the healthy orbitae of 372 patients from CT images in the bone and soft tissue windows using the Disior™ Bonelogic™ CMF Orbital software. (version 2.1.28). Methods: We analyzed the variables orbital volume, length, and area as a function of age and gender and compared bone and soft tissue windows. Results: For all variables, the intraclass correlation showed excellent agreement between the bone and soft tissue windows (p < 0.001). All variables showed higher values when calculated based on bone fenestration with, on average, 1 mL more volume, 0.35 mm more length, and 0.71 cm2 more area (p < 0.001). Across all age groups, men displayed higher values than women with, on average, 8.1 mL larger volume, a 4.78 mm longer orbit, and an 8.5 cm2 larger orbital area (p < 0.001). There was also a non-significant trend in all variables and both sexes toward growth with increasing age. Conclusions: These results mean that, due to the symmetry of the orbits in both the bone and soft tissue windows, the healthy orbit can be mirrored for surgical planning in the event of a fracture.

Basic Consideration for Facial Aging: Age-Related Changes of the Bony Orbit and Orbicularis Oculi Muscle in East Asians

BACKGROUND

Age-related changes in the periocular areas are mainly caused by anatomic changes of the bony orbit and orbicularis oculi muscle (OOM). To achieve effective rejuvenation, it is necessary to understand the age-related aspects of these anatomic changes.

OBJECTIVES

The aim of this study was to analyze the configuration of the bony orbit and OOM with computed tomography (CT) and to evaluate the effects of aging on these structures.

METHODS

A total of 220 orbits and OOMs of 110 Japanese participants (55 males, 55 females) aged 20 to 87 years were enrolled. The long diameter of the orbits, orbital ellipticity, OOM thickness, and OOM attachment to the inferior orbital rim were analyzed. These variables were statistically evaluated for their relationship with age.

RESULTS

The long diameter of the orbit was significantly longer in those over than in those under 60 years, with a moderate and significant positive correlation between orbital ellipticity and age. OOM thickness and age showed a strong negative correlation. The degree of OOM attachment to the inferior orbital rim decreased significantly with age.

CONCLUSIONS

This study showed that age-related changes of the bony orbit in Japanese individuals tended to be the same as those in Caucasians, but there were differences in the degree of changes observed. As a new finding in the Japanese population, the OOM not only thins with aging, but also gradually loosens from the facial bone. In the elderly, only the nasal side of the OOM was attached to the bone. In clinical applications, this knowledge could contribute to the development of cosmetic surgeries.

End-to-End Deep-Learning-Based Diagnosis of Benign and Malignant Orbital Tumors on Computed Tomography Images

Determining the nature of orbital tumors is challenging for current imaging interpretation methods, which hinders timely treatment. This study aimed to propose an end-to-end deep learning system to automatically diagnose orbital tumors. A multi-center dataset of 602 non-contrast-enhanced computed tomography (CT) images were prepared. After image annotation and preprocessing, the CT images were used to train and test the deep learning (DL) model for the following two stages: orbital tumor segmentation and classification. The performance on the testing set was compared with the assessment of three ophthalmologists. For tumor segmentation, the model achieved a satisfactory performance, with an average dice similarity coefficient of 0.89. The classification model had an accuracy of 86.96%, a sensitivity of 80.00%, and a specificity of 94.12%. The area under the receiver operating characteristics curve (AUC) of the 10-fold cross-validation ranged from 0.8439 to 0.9546. There was no significant difference on diagnostic performance of the DL-based system and three ophthalmologists (p > 0.05). The proposed end-to-end deep learning system could deliver accurate segmentation and diagnosis of orbital tumors based on noninvasive CT images. Its effectiveness and independence from human interaction allow the potential for tumor screening in the orbit and other parts of the body.

Effective encoder-decoder neural network for segmentation of orbital tissue in computed tomography images of Graves' orbitopathy patients

PURPOSE

To propose a neural network (NN) that can effectively segment orbital tissue in computed tomography (CT) images of Graves' orbitopathy (GO) patients.

METHODS

We analyzed orbital CT scans from 701 GO patients diagnosed between 2010 and 2019 and devised an effective NN specializing in semantic orbital tissue segmentation in GO patients' CT images. After four conventional (Attention U-Net, DeepLab V3+, SegNet, and HarDNet-MSEG) and the proposed NN train the various manual orbital tissue segmentations, we calculated the Dice coefficient and Intersection over Union for comparison.

RESULTS

CT images of the eyeball, four rectus muscles, the optic nerve, and the lacrimal gland tissues from all 701 patients were analyzed in this study. In the axial image with the largest eyeball area, the proposed NN achieved the best performance, with Dice coefficients of 98.2% for the eyeball, 94.1% for the optic nerve, 93.0% for the medial rectus muscle, and 91.1% for the lateral rectus muscle. The proposed NN also gave the best performance for the coronal image. Our qualitative analysis demonstrated that the proposed NN outputs provided more sophisticated orbital tissue segmentations for GO patients than the conventional NNs.

CONCLUSION

We concluded that our proposed NN exhibited an improved CT image segmentation for GO patients over conventional NNs designed for semantic segmentation tasks.

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.