The insular cortex is not insular in thyroid eye disease: neuroimaging revelations of central-peripheral system interaction.

BACKGROUND: Thyroid eye disease (TED) is highly correlated with dysregulated immunoendocrine status. The insular cortex was found to regulate peripheral inflammation and immunomodulation in mice. This study aimed to explore whether the insular cortex in patients with TED played a modulatory role including the aberrant brain functional alteration and its association with immunoendocrine status. METHODS: This study included 34 active patients (AP), 30 inactive patients (IP) with TED, and 45 healthy controls (HC) matched for age, sex, and educational level. Comprehensive clinical details (especially immunoendocrine markers) and resting-state functional magnetic resonance imaging data were collected from each participant. The amplitude of low-frequency fluctuation (ALFF) was used to probe the aberrant alterations of local neural activity. The seed-based functional connectivity (FC) analysis was used to explore the relationship between the insular cortex and each voxel throughout the whole brain. The correlation analysis was conducted to assess the association between insular neurobiomarkers and immunoendocrine parameters. RESULTS: When compared with the IP and HC groups, the AP group displayed significantly higher ALFF values in the right insular cortex (INS.R) and lower FC values between the INS.R and the bilateral cerebellum. None of the neurobiomarkers differed between the IP and HC groups. Besides, correlations between insular neurobiomarkers and immunoendocrine markers (free thyroxine, the proportion of T cells, and natural killer cells) were identified in both AP and IP groups. CONCLUSIONS: This study was novel in reporting that the dysregulation of the insular cortex activity in TED was associated with abnormal peripheral immunoendocrine status. The insular cortex might play a key role in central-peripheral system interaction in TED. Further research is crucial to enhance our understanding of the central-peripheral system interaction mechanisms involved in autoimmune diseases.

Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease.

BACKGROUND: Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model. METHODS: In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms. RESULTS: The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions. CONCLUSIONS: The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result.

White Matter Alterations of Visual Pathway in Thyroid Eye Disease: A Fixel-Based Analysis.

BACKGROUND: Thyroid eye disease (TED), particularly its sight-threatening complication, dysthyroid optic neuropathy (DON), profoundly impacts patients' visual health. The pathological changes in the white matter (WM) fibers within the intracranial visual pathway in TED have been infrequently studied. Understanding these changes holds crucial importance for exploring the pathogenesis and prognosis of TED. PURPOSE: To utilize fixel-based analysis (FBA) to clarify the type of microstructural damage occurring in the visual pathway in TED. STUDY TYPE: Prospective. SUBJECTS: 28 TED with DON patients (11 males and 17 females), 28 TED without DON (non-DON) patients (12 males and 16 females), and 28 healthy controls (HCs) (12 males and 16 females). FIELD STRENGTH/SEQUENCE: 3 T; multishell diffusion MRI using echo planar imaging. ASSESSMENT: Fiber density (FD) and fiber-bundle cross-section (FC) were calculated to characterize WM microstructural alteration in TED visual pathway. The correlations between FBA metrics and visual field index and mean deviation were examined. STATISTICAL TESTS: One-way analysis of variance, Kruskal-Wallis, t-tests, Mann-Whitney U, Chi-square, and Pearson correlation, were conducted with false discovery rate and family wise error corrections. Significance was set at P < 0.05. RESULTS: Both DON and non-DON groups showed significant FD loss in the right optic tract compared with HCs, with DON patients experiencing more severe FD loss. Only DON patients had FD loss in the right optic radiation (OR) compared with the non-DON patients and HCs, with no FC difference across groups. FD in DON patients' ORs significantly correlated with visual field index (r = 0.857) and mean deviation (r = 0.751). DATA CONCLUSION: Both DON and non-DON affect the WM microstructure of the visual pathway to varying extents. Visual field metrics can reflect the severity of FD damage to the OR in the visual pathway of DON patients. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.

Radiomics in ophthalmology: a systematic review.

BACKGROUND: Radiomics holds great potential in medical image analysis for various ophthalmic diseases. In recent times, there have been numerous endeavors in this area of research. This systematic review aims to provide a comprehensive assessment of the strengths and limitations of radiomics in ophthalmology. METHOD: Conforming to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, we conducted a systematic review with a pre-registered protocol (PROSPERO: CRD42023446317). We explored the PubMed, Embase, and Cochrane databases for original studies on this topic and made a comprehensive descriptive integration. Furthermore, the included studies underwent quality assessment by the radiomics quality score (RQS). RESULTS: A total of 41 articles from an initial search of 227 studies were finally selected for further analysis. These articles included research across five disease categories and covered seven imaging modalities. The radiomics models demonstrated robust performance, with area under the curve (AUC) values mostly falling within 0.7-1.0. The moderate RQS (mean score: 11.17/36) indicated that most studies were retrospectively, single-center analyses without external validation. CONCLUSIONS: Radiomics holds promising utility in the field of ophthalmology, assisting diagnosis, early-stage screening, and prognostication of treatment response. Artificial intelligence algorithms significantly contribute to the construction of radiomics models in ophthalmology. This study highlights the strengths and challenges of radiomics in ophthalmology and suggests potential avenues for future improvement. CLINICAL RELEVANCE STATEMENT: Radiomics represents a valuable approach for generating innovative imaging markers, enhancing efficiency in clinical diagnosis and treatment, and aiding decision-making in clinical contexts of many ophthalmic diseases, thereby improving overall patient prognosis. KEY POINTS: Radiomics has attracted extensive attention in the field of ophthalmology. Articles included five disease categories over seven imaging modalities, consistently yielding AUCs mostly above 0.7. Current research has few prospective and multi-center studies, underlining the necessity for future high-quality studies.

Immune-related visual dysfunction in thyroid eye disease: a combined orbital and brain neuroimaging study.

OBJECTIVES: To investigate the pathological interplay between immunity and the visual processing system (VPS) in thyroid eye disease (TED). METHODS: A total of 24 active patients (AP), 26 inactive patients (IP) of TED, and 27 healthy controls (HCs) were enrolled. Orbital magnetic resonance imaging (MRI) and resting-state functional MRI (rs-fMRI) were conducted for each participant. Multiple MRI parameters of the intraorbital optic nerve (ON) were assessed. The amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) were calculated. Correlation analyses were carried out on the above parameters and clinical characteristics. RESULTS: Visual functioning scores differentiated between the AP and IP groups. The ON subarachnoid space and ON sheath diameter were significantly higher in AP than in IP. Six vision-related brain regions were identified in TED patients compared with HCs, including right calcarine (CAL.R), right cuneus (CUN.R), left postcentral gyrus (PoCG.L), right middle temporal gyrus (MTG.R), left superior frontal gyrus (SFG.L), and left caudate (CAU.L). The brain activity of MTG.R, SFG.L, and CAU.L differentiated between the AP and IP groups. The correlation analysis revealed a close association among the vision-related brain regions, MRI parameters of ON, and clinical characteristics in AP and IP, respectively. CONCLUSIONS: Combined orbital and brain neuroimaging revealed abnormalities of the VPS in TED, which had a close correlation with immune statuses. Vision-related brain regions in TED might be possibly altered by peripheral immunity via a direct or indirect approach. CLINICAL RELEVANCE STATEMENT: The discovery of this study explained the disparity of visual dysfunction in TED patients with different immune statuses. With the uncovered neuroimaging markers, early detection and intervention of visual dysfunction could be achieved and potentially benefit TED patients. KEY POINTS: • Patients with different immune statuses of thyroid eye disease varied in the presentation of visual dysfunction. • The combined orbital and brain neuroimaging study identified six altered vision-related brain regions, which had a significant correlation with the MRI parameters of the intraorbital optic nerve and immunological characteristics. • Peripheral immunity might possibly give rise to alterations in the central nervous system part of the visual processing system via a direct or indirect approach.

Peripheral CD3+CD4+ T cells as indicators of disease activity in thyroid eye disease: age-dependent significance.

PURPOSE: To provide an in-depth analysis of the association of peripheral lymphocytes and the disease activity of thyroid eye disease (TED). METHODS: This retrospective study enrolled 65 active TED patients and 46 inactive TED patients. Comparative analyses of peripheral lymphocyte subsets were conducted between active and inactive patients. Subgroup analyses were performed based on sex, age, disease duration, and severity. Correlation analyses explored the associations between lymphocyte subsets and TED activity indicators. Prediction models for TED activity were established using objective indicators. RESULTS: Significantly elevated levels of CD3+CD4+ T cells were observed in active TED patients compared to inactive patients (P = 0.010). Subgroup analyses further revealed that this disparity was most prominent in females (P = 0.036), patients aged 50 years and younger (P = 0.003), those with long-term disease duration (P = 0.022), and individuals with moderate-to-severe disease (P = 0.021), with age exerting the most substantial impact. Subsequent correlation analysis confirmed the positive association between CD3+CD4+ T cells and the magnetic resonance imaging indicator of TED activity among patients aged 50 years and younger (P = 0.038). The combined prediction models for TED activity, established using objective indicators including CD3+CD4+ T cells, yielded areas under curve of 0.786 for all patients and 0.816 for patients aged 50 years and younger. CONCLUSIONS: Peripheral CD3+CD4+ T cells are associated with disease activity of TED, especially in patients aged 50 years and younger. Our study has deepened the understanding of the peripheral T cell profiles in TED patients.

The Clinical Intervention Effects of Evidence-based Nursing Measures in General Anesthesia Patients in the Postoperative Recovery Period.

Objective: This work investigated the clinical intervention effect of evidence-based nursing (EBN) measures for patients in the recovery stage after general anesthesia (GA), aiming to provide a nursing reference for patients in the recovery stage after surgery. Methods: The enrolled participants were 102 patients who underwent surgical treatment in our hospital from December 2021 to December 2022. According to the principle of randomized control, they were enrolled into an observation group (51 cases, Obs group) and a control group (51, cases, Ctrl group), and the general nursing methods and EBN measures were respectively implemented. The incidence of restlessness, complication rate, and nursing satisfaction were compared among patients. The recovery period and visual analog scale (VAS) were evaluated. Results: The eye-opening time, palm-holding time, and extubation time in the Obs group were shorter than those in the Ctrl group (P < .05). The incidence of agitation during convalescence under GA in the Obs group was significantly lower than in the Ctrl group, with a statistically significant difference among both groups (P < .05). Compared to the Ctrl group, the VAS score of patients in the Obs group receiving the EBN was lower at 6 h, 12 h, and 24 h after the surgery (P < .05). The patients in the Obs group presented a substantially lower complication rate and remarkably higher nursing satisfaction (P < .05). Conclusion: The application of EBN measures in patients after GA could effectively shorten the recovery time, lower the incidence of agitation and complication rate during the recovery, and improve nursing satisfaction.

Whole-orbit-based multiparametric assessment of disease activity of thyroid eye disease on Dixon MRI.

PURPOSE: This study aimed to explore the diagnostic value of whole-orbit-based multiparametric assessment on Dixon MRI for the evaluation of the thyroid eye disease (TED) activity. METHODS: The retrospective study enrolled patients diagnosed as TED and obtained their axial and coronal Dixon MRI scans. Multiparameters were assessed, including water fraction (WF), fat fraction (FF) of extraocular muscles (EOMs), orbital fat (OF), and lacrimal gland (LG). The thickness of OF and herniation of LG were also measured. Univariable and multivariable logistic regression was applied to construct prediction models based on single or multiple structures. Receiver operating characteristic (ROC) curve analysis was also implemented. RESULTS: Univariable logistic analysis revealed significant differences in water fraction (WF) of the superior rectus (P = 0.018), fat fraction (FF) of the medial rectus (P = 0.029), WF of OF (P = 0.004), and herniation of LG (P = 0.012) between the active and inactive TED phases. Multivariable logistic analysis and corresponding receiver operating characteristic curve (ROC) analysis of each structure attained the area under the curve (AUC) values of 0.774, 0.771, and 0.729 for EOMs, OF, and LG, respectively, while the combination of the four imaging parameters generated a final AUC of 0.909. CONCLUSIONS: Dixon MRI may be used for fine multiparametric assessment of multiple orbital structures. The whole-orbit-based model improves the diagnostic performance of TED activity evaluation.

Application of Quantitative MRI in Thyroid Eye Disease: Imaging Techniques and Clinical Practices.

Thyroid eye disease (TED) is a complex autoimmune disorder that impairs various orbital structures, leading to cosmetic damage and vision loss. Magnetic resonance imaging (MRI) is a fundamental diagnostic tool utilized in clinical settings of TED, for its accurate demonstration of orbital lesions and indication of disease conditions. The application of quantitative MRI has brought a new prospect to the management and research of TED, offering more detailed information on morphological and functional changes in the orbit. Therefore, many researchers concentrated on the implementation of different quantitative MRI techniques on TED for the exploration of clinical practices. Despite the abundance of studies utilizing quantitative MRI in TED, there remain considerable barriers and disputes on the best exploitation of this tool. This could possibly be attributed to the complexity of TED and the fast development of MRI techniques. It is necessary that clinical and radiological aspects of quantitative MRI in TED be better integrated into comprehensive insights. Hence, this review traces back 30 years of publications regarding quantitative MRI utilized in TED and elucidates this promising application in the facets of imaging techniques and clinical practices. We believe that a deeper understanding of the application of quantitative MRI in TED will enhance the efficacy of the multidisciplinary management of TED. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

A novel method of measuring proptosis with computed tomography.

BACKGROUND: Computed tomography (CT) can avoid interference factors and has been imported into some software to measure proptosis clinically as the golden standard. PURPOSE: To establish a new method for semi-automatically measuring the proptosis on CT and evaluate its accuracy and reproducibility. MATERIAL AND METHODS: A total of 50 orbital CT images were collected of healthy individuals, 25 patients with Graves ophthalmopathy (GO), and 25 patients with orbital fracture (OF). A new image processing software, MedrawHDC, was developed to semi-automatically measure the proptosis (MedrawHDC method). The classic radiological (CR) method (measuring proptosis with the software called Mimics) and MedrawHDC method were applied in all three groups (measured by observer S). Hertel's exophthalmometer (HE) method was also applied in the GO group. Moreover, two other observers were asked to measure the proptosis using MedrawHDC, to evaluate its reproducibility. RESULTS: The MedrawHDC method was highly consistent with the CR method in measuring proptosis (normal group: intraclass correlation coefficient [ICC] = 0.989; GO group: ICC = 0.979; OF group: ICC = 0.979). In the GO group, the value of proptosis measured by two radiological methods were consistent with that measured by the HE method (CR method: ICC = 0.703; MedrawHDC method: ICC = 0.697). Bland-Altman plots showed similar results. The measurements obtained by three observers were highly reproducible (ICC = 0.995). CONCLUSION: The newly established MedrawHDC method, with high accessibility, convenience, and repeatability, is reliable in assessing proptosis. It shows high potential for wide application, having clinical value for scientific evaluation of proptosis.

Orbital Soft Tissue Displacement After Blow-Out Fracture Repair Using Poly (L-Lactide-Co-Glycolide) Polymer Plates Based on Image Fusion Technique.

PURPOSE: To analyze the displacement of orbital soft tissue after blow-out fracture (BOF) repair with poly (L-lactide-co-glycolide) plates. MATERIALS AND METHODS: In this retrospective study, all patients who had undergone repair operations for orbital BOF from 2017 to 2021 were evaluated. Poly (L-lactide-co-glycolide) plates were used as repair materials. Preoperative and postoperative computed tomography images were integrated into the same coordinate system applying image fusion technique and were compared to determine the maximum displacement of orbital tissue after surgical repair. RESULTS: A total of 15 patients were included. Five were male, and 10 were female. Mean age was 33±16 years. Median waiting period was 18 (12-23) days. Six cases were medial wall fractures, 5 were floor fractures, and 4 were combined fractures. Maxillo-ethmoidal strut was involved in 4. Mean defect area was 176.52±108.48 mm 2 . Median interval between postoperative imaging examinations was 292 (223-600) days. Mean orbital tissue displacement was 2.6±1.8 mm. Using simple and multivariable linear regression analysis, the fracture defect area ( P =0.001) and maxillo-ethmoidal strut involvement ( P =0.013) were found to be significantly associated with orbital tissue displacement. Median orbital volume change was 0.804 (0.647-1.010) cm 3 . Average proptosis variation was 1.2±0.8 mm. CONCLUSIONS: Poly (L-lactide-co-glycolide) plates were more suitable for orbital BOF with small defect size. Those with large defect or maxillo-ethmoidal strut involved might have greater tissue displacements due to decline of supporting strength of poly (L-lactide-co-glycolide) plates.

Noninvasive Machine Learning Screening Model for Dacryocystitis Based on Ocular Surface Indicators.

BACKGROUND: Dacryocystitis is an orbital disease that can be easily misdiagnosed. The most common diagnostic tools for dacryocystitis are computed tomography, lacrimal duct angiography, and lacrimal tract irrigation. Yet, those are invasive methods, which are not conducive to extensive screening. OBJECTIVE: To explore the significance of ocular surface indicators and demographic data in the screening of dacryocystitis. MATERIALS AND METHODS: Data were prospectively collected from 56 patients with dacryocystitis (56 eyes) and 56 healthy individuals. Collected indicators included demographic information (gender, age), ocular surface data of tear meniscus height, objective scatter index (OSI), and clinical diagnosis. The model features were screened out by machine learning to establish a dacryocystitis screening model. RESULTS: Tear meniscus height, OSI_maximum Lyapunov exponent, basic OSI, median of OSI, mean of OSI, slope coefficient of OSI linear regression, coefficient of variation in OSI, interquartile range of OSI, and other 8 parameters were used as model parameters to establish a dacryocystitis screening model with an overall detection accuracy of 85.71%. CONCLUSIONS: This new screening model that is based on ocular surface indicators provides a new option for noninvasive screening of dacryocystitis.

Explainable Prediction of Dacryocystitis From Noninvasive Ocular Indicators Using Deep Stacked Network and The Shapley Additive Explanations Approach.

ABSTRACT: Dacryocystitis diagnosis is important for preventing rapid blurring and vision loss. Existing state-of-the-art methods focus on routine clinical examinations and objective scattering index-based statistical analysis. Such approaches are invasive operations or lack quantitative indicators, and their application is limited. in addition, little attention has been paid to the explainability and clinical utility of models. This paper proposes an explainable dacryocystitis prediction model from noninvasive ocular indicators. The proposed model is based on an deep stacked network with 4 improvements: a multivariable feature extraction module, obtaining comprehensive predictive factors including the quantitative ocular indictors, conventional texture features, and deep learning features from shallow to deep convolutional layers; a multifeature fusion and attribute selection module based on the ReliefF method, guiding the network to focus on useful information at variables; Decision curve analysis the model is introduced into the model to evaluates the risks and benefits; and appending a SHapley Additive exPlanations (SHAP) module to the framework to automatically and efficiently interpret the prediction of the models. By integrating the above improvements in series, the models' performances are gradually enhanced. Real labeled data samples are used to train and test the model, and our model achieves high accuracy and reliability.

Urban development enhances soil organic carbon storage through increasing urban vegetation.

Anthropogenic activities can lead to the loss of soil organic carbon (SOC) or improve its storage, hence they have the potential to exacerbate or help mitigate climate change. Urban expansion results in an initial loss of soil carbon, but long-term SOC changes during urban development are poorly understood. Herein, we studied SOC changes in the suburban and urban areas of cities with high levels of urbanization based on a long-term resampling campaign in Beijing, and a compilation of SOC content data from 21 other cities with high levels of urbanization across China over the past three decades. Our results revealed that the SOC of topsoils decreased by 17.2% in the suburban areas and increased by 104.4% in the urban areas of cities with high levels of urbanization. The changes in SOC were positively correlated with the changes in vegetation coverage and productivity. Partial least square method structural equation model analyses showed that changes in vegetation could directly affect SOC changes, and the changes in vegetation coverage and productivity were induced by human activities and climate changes in Beijing. The topsoils in the urban areas of cities with high levels of urbanization can act as carbon sinks due to the increase in vegetation. This study can help improve our understanding of the role of the SOC content of cities within the global C cycle and provide suggestions for achieving the goal of carbon neutrality in China.

Dacryoendoscopy-assisted incision of Hasner's valve under nasoendoscopy for membranous congenital nasolacrimal duct obstruction after probing failure: a retrospective study.

BACKGROUND: To introduce a treatment option: dacryoendoscopy-assisted incision of Hasner's valve under nasoendoscopy and assess its efficacy in treating membranous congenital nasolacrimal duct obstruction (CNLDO) in children older than 1 year with history of initial probing failure. METHODS: 52 eyes of 43 children with membranous CNLDO who underwent dacryoendoscopy-assisted incision of Hasner's valve under nasoendoscopy between May 2012 and October 2020 were enrolled in this retrospective study. All participants were older than 1 year and all the eyes had gone through probing at least once but still had symptoms of epiphora and discharge. Surgical success was defined as a normal fluorescein dye disappearance test (FDDT) and the absence of pre-operation complaints, including epiphora, muco-purulent discharge, increased tear lake or the symptoms of acute infection such as acute dacryocystitis. Patients' demographics, clinical features and follow-up outcomes were evaluated. RESULTS: Of all these operated cases, surgical success was achieved in 52/52 eyes without any early or late complications. The overall success rate was 100%. CONCLUSIONS: Dacryoendoscopy-assisted incision of Hasner's valve under nasoendoscopy is an effective and minimally invasive surgical treatment in membranous CNLDO patients with initial probing failure.

Detection of active and inactive phases of thyroid-associated ophthalmopathy using deep convolutional neural network.

BACKGROUND: This study aimed to establish a deep learning system for detecting the active and inactive phases of thyroid-associated ophthalmopathy (TAO) using magnetic resonance imaging (MRI). This system could provide faster, more accurate, and more objective assessments across populations. METHODS: A total of 160 MRI images of patients with TAO, who visited the Ophthalmology Clinic of the Ninth People's Hospital, were retrospectively obtained for this study. Of these, 80% were used for training and validation, and 20% were used for testing. The deep learning system, based on deep convolutional neural network, was established to distinguish patients with active phase from those with inactive phase. The accuracy, precision, sensitivity, specificity, F1 score and area under the receiver operating characteristic curve were analyzed. Besides, visualization method was applied to explain the operation of the networks. RESULTS: Network A inherited from Visual Geometry Group network. The accuracy, specificity and sensitivity were 0.863±0.055, 0.896±0.042 and 0.750±0.136 respectively. Due to the recurring phenomenon of vanishing gradient during the training process of network A, we added parts of Residual Neural Network to build network B. After modification, network B improved the sensitivity (0.821±0.021) while maintaining a good accuracy (0.855±0.018) and a good specificity (0.865±0.021). CONCLUSIONS: The deep convolutional neural network could automatically detect the activity of TAO from MRI images with strong robustness, less subjective judgment, and less measurement error. This system could standardize the diagnostic process and speed up the treatment decision making for TAO.

A clinical decision model based on machine learning for ptosis.

BACKGROUND: To establish a decision model based on two- (2D) and three-dimensional (3D) eye data of patients with ptosis for developing personalized surgery plans. METHODS: Data of this retrospective, case-control study was collected from March 2019 to June 2019 at the Department of Ophthalmology, Shanghai Ninth People's Hospital, and then the patients were followed up for 3 months. One hundred fifty-two complete feature eyes from 100 voluntary patients with ptosis and satisfactory surgical results were selected, with 48 eyes excluded due to any severe condition or improper collection and shooting angle. Three experimental schemes were set as follows: use 2D distance alone, use 3D distance alone, and use two distances at the same time. The five most common evaluation indicators used in the binary classification problem to test the decision model were accuracy (ACC), precision, recall, F1-score, and area under the curve (AUC). RESULTS: For diagnostic discrimination, recall of "3D", "2D" and "Both" schemes were 0.875, 0.875 and 0.938 respectively. And precision of the three schemes were 0.8333, 0.7778 and 1.0000 for the surgical procedure classification. Values of "Both" scheme that combined 2D and 3D data were the highest in two classifications. CONCLUSIONS: In this study, 3D eye data are introduced into clinical practice to construct a decision model for ptosis surgery. Our decision model presents exceptional prediction effect, especially when 2D and 3D data employed jointly.

Automatic orbital computed tomography coordinating method and quantitative error evaluation based on signed distance field.

BACKGROUND: Orbital computed tomography (CT) is commonly used for the diagnosis and digital evaluation of orbital diseases. Yet, this approach requires longer scanning time, increased radiation exposure, and, especially, difficult patient positioning that can affect judgment and data processing. According to high-quality research on orbital imaging, computer-assisted surgery, and artificial intelligent diagnostic development, the correction of a coordinate system is a necessary procedure. Nevertheless, existing manual calibration methods are challenging to reproduce and there is no objective evaluation system for errors. PURPOSE: To establish a method for automatic calibration of orbital CT images and implementation of quantitative error evaluation. MATERIAL AND METHODS: A standard three-dimensional (3D) orbit model was manually adjusted, and optimized orbital models were reconstructed based on the initial registration of the skull-bound directed bounding box and the registration of the mutual information method. The calibration error was calculated based on the signed distance field. Seventeen cases of orbital CT were quantitatively evaluated. RESULTS: A new method for automatic calibration and quantitative error evaluation for orbital CT was established. The calibrated model error with ±2 mm accounted for 81.61% ± 6.91% of the total models, and the error of ±1 mm accounted for 53.49% ± 7.07% of the total models. CONCLUSION: This convenient tool for orbital CT automatic calibration may promote the related quantitative research based on orbital CT. The automated operation and small error are beneficial to the popularization and application of the tool, and the quantitative evaluation facilitates other coordinate systems.

Predictors for Surgeries With the Endoscope-Navigation System for Traumatic Optic Neuropathy and its Clinical Assessment.

BACKGROUND: To assess surgeries with the endoscope-navigation system (ENS) in patients who underwent traumatic optic neuropathy (TON) and find predictors for best corrected visual acuity (BCVA) outcomes. METHODS: The clinical data of 96 consecutive TON patients (96 eyes) who underwent decompression surgery with ENS in the Department of Ophthalmology, Shanghai Ninth People's Hospital, from January 2013 to December 2019 were retrospectively reviewed and analyzed. A binary logistic regression was performed to establish a predictive model for BCVA after treatment as TON outcome. RESULTS: By practicing ENS, 49/96 (51.0%) TON patients got improvement in BCVA, whereas the improvement rate of patients with BCVA of light perception or better was 72.5% (29/40). Hemorrhage within the postethmoid and/or sphenoid sinus, orbital fracture, time interval between trauma and treatment, and BCVA before treatment were predictors for BCVA improvement in TON patients by practicing ENS surgery. The area under raw current curves of the predictive model was 0.826. CONCLUSIONS: Surgeries with the ENS showed positive outcomes for TON patients, especially for those with better BCVA before treatment, shorter time interval between trauma and treatment, without orbital fracture or hemorrhage within the postethmoid and/or sphenoid sinus.

Deep Convolutional Neural Networks for Automatic Detection of Orbital Blowout Fractures.

Orbital blow out fracture is a common disease in emergency department and a delay or failure in diagnosis can lead to permanent visual changes. This study aims to evaluate the ability of an automatic orbital blowout fractures detection system based on computed tomography (CT) data.Orbital CT scans of adult orbital blowout fractures patients and normal cases were obtained from Shanghai Ninth People's Hospital between January and March 2017. The region of fractures was annotated using 3D Slicer. The Inception V3 convolutional neural networks were constructed utilizing the Python programming language with PyTorch as the framework to extract high dimension features from each slice in a CT scan. These extracted features are processed through a XGBoost model to make the final differentiation of fracture cases and nonfracture ones. Accuracy, receiver operating characteristics, and area under the curve were evaluated.This study used 94 CT scans diagnosed with orbital blowout fractures and 94 healthy control cases. The automatic detection system showed accuracy of 92% in single-image classification and 87% in patient level detection. The area under the receiver operating characteristic curve was 0.9574.Using a deep learning-based automatic detection system of orbital blowout fracture can accurately detect and classify orbital blowout fractures from CT scans. The convolutional neural networks model combined with an accurate annotation system could achieve good performance in a small dataset. Further studies with large and multicenter data are required to refine this technology for possible clinical applications.