An Intelligent Diagnostic System for Thyroid-Associated Ophthalmopathy Based on Facial Images

Advances in artificial intelligence in thyroid-associated ophthalmopathy

Thyroid-associated ophthalmopathy (TAO), also referred to as Graves' ophthalmopathy, is a medical condition wherein ocular complications arise due to autoimmune thyroid illness. The diagnosis of TAO, reliant on imaging, typical ocular symptoms, and abnormalities in thyroid function or thyroid-associated antibodies, is generally graded and staged. In recent years, Artificial intelligence(AI), particularly deep learning(DL) technology, has gained widespread use in the diagnosis and treatment of ophthalmic diseases. This paper presents a discussion on specific studies involving AI, specifically DL, in the context of TAO, highlighting their applications in TAO diagnosis, staging, grading, and treatment decisions. Additionally, it addresses certain limitations in AI research on TAO and potential future directions for the field.

An ensemble deep learning diagnostic system for determining Clinical Activity Scores in thyroid-associated ophthalmopathy: integrating multi-view multimodal images from anterior segment slit-lamp photographs and facial images

Background

Thyroid-associated ophthalmopathy (TAO) is the most prevalent autoimmune orbital condition, significantly impacting patients' appearance and quality of life. Early and accurate identification of active TAO along with timely treatment can enhance prognosis and reduce the occurrence of severe cases. Although the Clinical Activity Score (CAS) serves as an effective assessment system for TAO, it is susceptible to assessor experience bias. This study aimed to develop an ensemble deep learning system that combines anterior segment slit-lamp photographs of patients with facial images to simulate expert assessment of TAO.

Method

The study included 156 patients with TAO who underwent detailed diagnosis and treatment at Shanxi Eye Hospital Affiliated to Shanxi Medical University from May 2020 to September 2023. Anterior segment slit-lamp photographs and facial images were used as different modalities and analyzed from multiple perspectives. Two ophthalmologists with more than 10 years of clinical experience independently determined the reference CAS for each image. An ensemble deep learning model based on the residual network was constructed under supervised learning to predict five key inflammatory signs (redness of the eyelids and conjunctiva, and swelling of the eyelids, conjunctiva, and caruncle or plica) associated with TAO, and to integrate these objective signs with two subjective symptoms (spontaneous retrobulbar pain and pain on attempted upward or downward gaze) in order to assess TAO activity.

Results

The proposed model achieved 0.906 accuracy, 0.833 specificity, 0.906 precision, 0.906 recall, and 0.906 F1-score in active TAO diagnosis, demonstrating advanced performance in predicting CAS and TAO activity signs compared to conventional single-view unimodal approaches. The integration of multiple views and modalities, encompassing both anterior segment slit-lamp photographs and facial images, significantly improved the prediction accuracy of the model for TAO activity and CAS.

Conclusion

The ensemble multi-view multimodal deep learning system developed in this study can more accurately assess the clinical activity of TAO than traditional methods that solely rely on facial images. This innovative approach is intended to enhance the efficiency of TAO activity assessment, providing a novel means for its comprehensive, early, and precise evaluation.

Intermittent Fasting and Vitamin E Supplementation Attenuates Hypothyroidism-Associated Ophthalmopathy

Visualization is a complex-integrated procedure of the eyes and brain that allows to see this colorful world. Hypothyroidism-associated ophthalmopathy (HAO), often known as dry eyes, swelling around the eyes, blurred vision, glaucoma, and cataracts, are some eye-related issues caused by hypothyroidism. Yet there is no permanent cure for hypothyroidism; taking medicine throughout life is the only solution to keep its harmful effects under control. This study used intermittent fasting (IF) and vitamin E (Vit.E) supplementation to prevent hypothyroidism-associated ophthalmopathy. This study hypothesized that intermittent fasting-like diet regimens and vitamin supplementation should reduce the propagation of HAO by its antioxidant potential. In the present study, experimental animals are divided into five groups: normal, hypothyroidism control, dual, Vit. E, and IF. Hypothyroidism is generated in the experimental groups by taking propylthiouracil (PTU) for 24 days while also taking IF and Vit. E supplements. The hypothyroid-induced experimental animals demonstrated an increase in IOP and lipid peroxidation while thyroid hormone levels depicted a massive decline which is a clear denotation of the effects of the thyroid on eyes and lifestyle. Ancient Ayurveda inspires these proposed therapies and has successfully reduced all the damage to the thyroid gland and the eye.

Identification of immune-related regulatory networks and diagnostic biomarkers in thyroid eye disease

BACKGROUND

Thyroid eye disease (TED) is an orbit-associated autoimmune inflammatory disorder intricately linked to immune dysregulation. Complete pathogenesis of TED remains elusive. This work aimed to mine pathogenesis of TED from immunological perspective and identify diagnostic genes.

METHODS

Gene expression microarray data for TED patients were downloaded from Gene Expression Omnibus, immune-related genes (IRGs) were from ImmPort database, and TED-related transcription factors (TFs) were from Cirtrome Cancer database. Differential analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Regulatory networks of TFs and IRGs were constructed with Cytoscape. Diagnostic biomarkers in TED were identified through LASSO. Immune cell infiltration analysis was performed using CIBERSORT.

RESULTS

Twenty-three immune-related DEmRNAs were revealed and were primarily enriched in humoral immune response, positive regulation of inflammatory response, IL-17, and TNF pathways. Co-expression regulatory network included four TFs and 16 immune-related DEmRNAs. Seven diagnostic genes were identified, with Area Under the Curve (AUC) of 0.993 for training set and AUC value of 0.836 for validation set. TED patients exhibited elevated infiltration levels by macrophages M2, mast cells, and CD8 T cells among 22 immune cell types, whereas macrophages M2 and mast cells resting were significantly lower than normal group.

CONCLUSIONS

The seven feature genes had high diagnostic value for TED patients. Our work explored regulatory network and diagnostic biomarkers, laying theoretical basis for TED diagnosis and treatment.

Global research on artificial intelligence in thyroid-associated ophthalmopathy: A bibliometric analysis

Purpose

To provide an overview of global publications on artificial intelligence (AI) in thyroid-associated ophthalmopathy (TAO) through bibliometric analysis.

Methods

Publications related to AI in TAO from inception until April 2023 were retrieved from the Web of Science database. The trends of publications and citations, publishing performance, collaboration among countries and institutions, and the funding agencies, relevant research domains, leading journals, hotspots and their evolution were identified.

Results

A total of 55 publications were included for analysis. The number of publications and citations continued to grow since 1998, with a significant acceleration of growth after 2020. China is the most productive country with the highest number of productive institutions, followed by the United States. European countries have the most extensive collaboration. The most relevant research domain was radiology, nuclear medicine & medical imaging. The European Journal of Radiology was one of the most productive journals, with the most influential articles published. "Thyroid-associated ophthalmopathy" and "neural network" maintain hotspots during the entire period. Studies were more focused on clinical features during 1998 and 2016, clinical features and medical data during 2017 and 2020, and medical data and AI techniques during 2021 and 2023.

Conclusions

This study summarized the global research status regarding AI in TAO in terms of trends, countries, institutions, research domains, journals, and key topics. AI has shown great potential in TAO. Sponsored by funding agencies such as NSFC, China has become the most productive country in the field of AI in TAO. Our findings help researchers better understand the development of this field and provide valuable clues for future research directions.

A Deep Learning Model for Screening Computed Tomography Imaging for Thyroid Eye Disease and Compressive Optic Neuropathy

Purpose

Thyroid eye disease (TED) is an autoimmune condition with an array of clinical manifestations, which can be complicated by compressive optic neuropathy. It is important to identify patients with TED early to ensure close monitoring and treatment to prevent potential permanent disability or vision loss. Deep learning artificial intelligence (AI) algorithms have been utilized in ophthalmology and in other fields of medicine to detect disease. This study aims to introduce a deep learning model to evaluate orbital computed tomography (CT) images for the presence of TED and potential compressive optic neuropathy.

Design

Retrospective review and deep learning algorithm modeling.

Subjects

Patients with TED with dedicated orbital CT scans and with an examination by an oculoplastic surgeon over a 10-year period at a single academic institution. Patients with no TED and normal CTs were used as normal controls. Those with other diagnoses, such as tumors or other inflammatory processes, were excluded.

Methods

Orbital CTs were preprocessed and adopted for the Visual Geometry Group-16 network to distinguish patients with no TED, mild TED, and severe TED with compressive optic neuropathy. The primary model included training and testing of all 3 conditions. Binary model performance was also evaluated. An oculoplastic surgeon was also similarly tested with single and serial images for comparison.

Main Outcome Measures

Accuracy of deep learning model discernment of region of interest for CT scans to distinguish TED versus normal control, as well as TED with clinical signs of optic neuropathy.

Results

A total of 1187 photos from 141 patients were used to develop the AI model. The primary model trained on patients with no TED, mild TED, and severe TED had 89.5% accuracy (area under the curve: range, 0.96-0.99) in distinguishing patients with these clinical categories. In comparison, testing of an oculoplastic surgeon in these 3 categories showed decreased accuracy (70.0% accuracy in serial image testing).

Conclusions

The deep learning model developed in the study can accurately detect TED and further detect TED with clinical signs of optic neuropathy based on orbital CT. The model proved superior compared with human expert grading. With further optimization and validation, this TED deep learning model could help guide frontline health care providers in the detection of TED and help stratify the urgency of a referral to an oculoplastic surgeon and endocrinologist.

Financial Disclosures

The authors have no proprietary or commercial interest in any materials discussed in this article.

Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review

Background

There is emerging evidence which suggests the utility of artificial intelligence (AI) in the diagnostic assessment and pre-treatment evaluation of thyroid eye disease (TED). This scoping review aims to (1) identify the extent of the available evidence (2) provide an in-depth analysis of AI research methodology of the studies included in the review (3) Identify knowledge gaps pertaining to research in this area.

Methods

This review was performed according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA). We quantify the diagnostic accuracy of AI models in the field of TED assessment and appraise the quality of these studies using the modified QUADAS-2 tool.

Results

A total of 13 studies were included in this review. The most common AI models used in these studies are convolutional neural networks (CNN). The majority of the studies compared algorithm performance against healthcare professionals. The overall risk of bias and applicability using the modified Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool led to most of the studies being classified as low risk, although higher deficiency was noted in the risk of bias in flow and timing.

Conclusions

While the results of the review showed high diagnostic accuracy of the AI models in identifying features of TED relevant to disease assessment, deficiencies in study design causing study bias and compromising study applicability were noted. Moving forward, limitations and challenges inherent to machine learning should be addressed with improved standardized guidance around study design, reporting, and legislative framework.

Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions

Background: The use of artificial intelligence (AI) in health care has grown exponentially with the promise of facilitating biomedical research and enhancing diagnosis, treatment, monitoring, disease prevention, and health care delivery. We aim to examine the current state, limitations, and future directions of AI in thyroidology. Summary: AI has been explored in thyroidology since the 1990s, and currently, there is an increasing interest in applying AI to improve the care of patients with thyroid nodules (TNODs), thyroid cancer, and functional or autoimmune thyroid disease. These applications aim to automate processes, improve the accuracy and consistency of diagnosis, personalize treatment, decrease the burden for health care professionals, improve access to specialized care in areas lacking expertise, deepen the understanding of subtle pathophysiologic patterns, and accelerate the learning curve of less experienced clinicians. There are promising results for many of these applications. Yet, most are in the validation or early clinical evaluation stages. Only a few are currently adopted for risk stratification of TNODs by ultrasound and determination of the malignant nature of indeterminate TNODs by molecular testing. Challenges of the currently available AI applications include the lack of prospective and multicenter validations and utility studies, small and low diversity of training data sets, differences in data sources, lack of explainability, unclear clinical impact, inadequate stakeholder engagement, and inability to use outside of the research setting, which might limit the value of their future adoption. Conclusions: AI has the potential to improve many aspects of thyroidology; however, addressing the limitations affecting the suitability of AI interventions in thyroidology is a prerequisite to ensure that AI provides added value for patients with thyroid disease.

Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding

(1) Background: We constructed scores for moderate-to-severe and muscle-predominant types of Graves’ orbitopathy (GO) risk prediction based on initial ophthalmic findings. (2) Methods: 400 patients diagnosed with GO and followed up at both endocrinology and ophthalmology clinics with at least 6 months of follow-up. The Score for Moderate-to-Severe type of GO risk Prediction (SMSGOP) and the Score for Muscle-predominant type of GO risk Prediction (SMGOP) were constructed using the machine learning-based automatic clinical score generation algorithm. (3) Results: 55.3% were classified as mild type and 44.8% were classified as moderate-to-severe type. In the moderate-to-severe type group, 32.3% and 12.5% were classified as fat-predominant and muscle-predominant type, respectively. SMSGOP included age, central diplopia, thyroid stimulating immunoglobulin, modified NOSPECS classification, clinical activity score and ratio of the inferior rectus muscle cross-sectional area to total orbit in initial examination. SMGOP included age, central diplopia, amount of eye deviation, serum FT4 level and the interval between diagnosis of GD and GO in initial examination. Scores ≥46 and ≥49 had predictive value, respectively. (4) Conclusions: This is the first study to analyze factors in initial findings that can predict the severity of GO and to construct scores for risk prediction for Korean. We set the predictive scores using initial findings.

Research progress and application of artificial intelligence in thyroid associated ophthalmopathy

Thyroid-associated ophthalmopathy (TAO) is a complicated orbitopathy related to dysthyroid, which severely destroys the facial appearance and life quality without medical interference. The diagnosis and management of thyroid-associated ophthalmopathy are extremely intricate, as the number of professional ophthalmologists is limited and inadequate compared with the number of patients. Nowadays, medical applications based on artificial intelligence (AI) algorithms have been developed, which have proved effective in screening many chronic eye diseases. The advanced characteristics of automated artificial intelligence devices, such as rapidity, portability, and multi-platform compatibility, have led to significant progress in the early diagnosis and elaborate evaluation of these diseases in clinic. This study aimed to provide an overview of recent artificial intelligence applications in clinical diagnosis, activity and severity grading, and prediction of therapeutic outcomes in thyroid-associated ophthalmopathy. It also discussed the current challenges and future prospects of the development of artificial intelligence applications in treating thyroid-associated ophthalmopathy.

Automatic measurement of exophthalmos based orbital CT images using deep learning

Introduction: Objective, accurate, and efficient measurement of exophthalmos is imperative for diagnosing orbital diseases that cause abnormal degrees of exophthalmos (such as thyroid-related eye diseases) and for quantifying treatment effects. Methods: To address the limitations of existing clinical methods for measuring exophthalmos, such as poor reproducibility, low reliability, and subjectivity, we propose a method that uses deep learning and image processing techniques to measure the exophthalmos. The proposed method calculates two vertical distances; the distance from the apex of the anterior surface of the cornea to the highest protrusion point of the outer edge of the orbit in axial CT images and the distance from the apex of the anterior surface of the cornea to the highest protrusion point of the upper and lower outer edges of the orbit in sagittal CT images. Results: Based on the dataset used, the results of the present method are in good agreement with those measured manually by clinicians, achieving a concordance correlation coefficient (CCC) of 0.9895 and an intraclass correlation coefficient (ICC) of 0.9698 on axial CT images while achieving a CCC of 0.9902 and an ICC of 0.9773 on sagittal CT images. Discussion: In summary, our method can provide a fully automated measurement of the exophthalmos based on orbital CT images. The proposed method is reproducible, shows high accuracy and objectivity, aids in the diagnosis of relevant orbital diseases, and can quantify treatment effects.

Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy

Although the clinical activity score (CAS) is a validated scoring system for identifying disease activity of thyroid-associated orbitopathy (TAO), it may produce differing results depending on the evaluator, and an experienced ophthalmologist is required for accurate evaluation. In this study, we developed a machine learning (ML)-assisted system to mimic an expert's CAS assessment using digital facial images and evaluated its accuracy for predicting the CAS and diagnosing active TAO (CAS ≥ 3). An ML-assisted system was designed to assess five CAS components related to inflammatory signs (redness of the eyelids, redness of the conjunctiva, swelling of the eyelids, inflammation of the caruncle and/or plica, and conjunctival edema) in patients' facial images and to predict the CAS by considering two components of subjective symptoms (spontaneous retrobulbar pain and pain on gaze). To train and test the system, 3,060 cropped images from 1020 digital facial images of TAO patients were used. The reference CAS for each image was scored by three ophthalmologists, each with > 15 years of clinical experience. We repeated the experiments for 30 randomly split training and test sets at a ratio of 8:2. The sensitivity and specificity of the ML-assisted system for diagnosing active TAO were 72.7% and 83.2% in the test set constructed from the entire dataset. For the test set constructed from the dataset with consistent results for the three ophthalmologists, the sensitivity and specificity for diagnosing active TAO were 88.1% and 86.9%. In the test sets from the entire dataset and from the dataset with consistent results, 40.0% and 49.9% of the predicted CAS values were the same as the reference CAS, respectively. The system predicted the CAS within 1 point of the reference CAS in 84.6% and 89.0% of cases when tested using the entire dataset and in the dataset with consistent results, respectively. An ML-assisted system estimated the clinical activity of TAO and detect inflammatory active TAO with reasonable accuracy. The accuracy could be improved further by obtaining more data. This ML-assisted system can help evaluate the disease activity consistently as well as accurately and enable the early diagnosis and timely treatment of active TAO.

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.