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Shao J, Cao J, Wang C, Xu P, Lou L, Ye J. Automatic Measurement and Comparison of Normal Eyelid Contour by Age and Gender Using Image-Based Deep Learning. OPHTHALMOLOGY SCIENCE 2024; 4:100518. [PMID: 38881605 PMCID: PMC11179404 DOI: 10.1016/j.xops.2024.100518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/03/2024] [Accepted: 03/12/2024] [Indexed: 06/18/2024]
Abstract
Purpose This study aimed to propose a fully automatic eyelid measurement system and compare the contours of both the upper and lower eyelids of normal individuals according to age and gender. Design Prospective study. Participants Five hundred and forty healthy Chinese aged 0 to 79 years in a tertiary hospital were included. Methods Facial images in the primary gazing position were used to train and test the proposed automatic system for eye recognition and eye segmentation. According to the 10-millimeter diameter circular marker, measurements were transformed from pixel sizes into factual distances. Main Outcome Measures Midpupil lid distances (MPLDs) every 15° of all participants were automatically measured in both genders (30 males and 30 females in each age group) by the proposed deep learning (DL)-based system. Intraclass correlation coefficients (ICCs) were performed to assess the agreement between the automatic and manual margin reflex distances (MRDs). The eyelid contour, eyelid asymmetry, and palpebral fissure obliquity were analyzed using MPLD, temporal-versus-nasal MPLD ratio, and the angle between the inner and outer canthi, respectively. Results The measurement of MRDs by the automatic system excellently agreed with that of the expert, with ICCs ranging from 0.863 to 0.886. As the age of the participants increased, the values of MPLDs reached a peak in those in their 20s or 30s and then gradually decreased at all angles. The temporal sector showed greater changes in MPLDs than the nasal sector, and the changes were more significant in females than in males. The maximum value of palpebral fissure obliquity appeared before 10 years in both genders and remained relatively stable after the 20s (P > 0.05). Conclusions The proposed DL-based eyelid analysis system allowed automatic, accurate, and comprehensive measurement of the eyelid contour. The refinement of eyelid shape quantification could be beneficial for future objective assessment preocular and postocular plastic surgery. Financial Disclosures The authors have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Ji Shao
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Jing Cao
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Changjun Wang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Peifang Xu
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Lixia Lou
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
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Cai Y, Zhang X, Cao J, Grzybowski A, Ye J, Lou L. Application of artificial intelligence in oculoplastics. Clin Dermatol 2024; 42:259-267. [PMID: 38184122 DOI: 10.1016/j.clindermatol.2023.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2024]
Abstract
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.
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Affiliation(s)
- Yilu Cai
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Xuan Zhang
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Jing Cao
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - Juan Ye
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Lixia Lou
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China.
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Nam Y, Song T, Lee J, Lee JK. Development of a neural network-based automated eyelid measurement system. Sci Rep 2024; 14:1202. [PMID: 38216653 PMCID: PMC10786902 DOI: 10.1038/s41598-024-51838-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 01/10/2024] [Indexed: 01/14/2024] Open
Abstract
The purpose of this study was to assess the clinical utility and reliability of an automated eyelid measurement system utilizing neural network (NN) technology. Digital images of the eyelids were taken from a total of 300 subjects, comprising 100 patients with Graves' orbitopathy (GO), 100 patients with ptosis, and 100 controls. An automated measurement system based on NNs was developed to measure margin-reflex distance 1 and 2 (MRD1 and MRD2), as well as the lengths of the upper and lower eyelids. The results were then compared with values measured using the manual technique. Automated measurements of MRD1, MRD2, upper eyelid length, and lower eyelid length yielded values of 3.2 ± 1.7 mm, 6.0 ± 1.4 mm, 32.9 ± 6.1 mm, and 29.0 ± 5.6 mm, respectively, showing a high level of agreement with manual measurements. To evaluate the morphometry of curved eyelids, the distance from the midpoint of the intercanthal line to the eyelid margin was measured. The minimum number of divisions for detecting eyelid abnormalities was determined to be 24 partitions (15-degree intervals). In conclusion, an automated NN-based measurement system could provide a straightforward and precise method for measuring MRD1 and MRD2, as well as detecting morphological abnormalities in the eyelids.
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Affiliation(s)
- Yoonsoo Nam
- Department of Ophthalmology, Chung-Ang University College of Medicine, Chung-Ang University Hospital, 102 Heukseok-ro, Dongjak-gu, Seoul, 06973, Korea
| | - Taekyung Song
- Department of Artificial Intelligence, Chung-Ang University, Seoul, Korea
| | - Jaesung Lee
- Department of Artificial Intelligence, Chung-Ang University, Seoul, Korea.
| | - Jeong Kyu Lee
- Department of Ophthalmology, Chung-Ang University College of Medicine, Chung-Ang University Hospital, 102 Heukseok-ro, Dongjak-gu, Seoul, 06973, Korea.
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Feng Q, Liu S, Peng JX, Yan T, Zhu H, Zheng ZJ, Feng HC. Deep learning-based automatic sella turcica segmentation and morphology measurement in X-ray images. BMC Med Imaging 2023; 23:41. [PMID: 36964517 PMCID: PMC10039601 DOI: 10.1186/s12880-023-00998-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 03/14/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may introduce subjective bias. This paper aims to develop and evaluate a deep learning-based model for automatic segmentation and measurement of sella turcica in cephalometric radiographs. METHODS 1129 images were used to develop a deep learning-based segmentation network for automatic sella turcica segmentation. Besides, 50 images were used to test the generalization ability of the model. The performance of the segmented network was evaluated by the dice coefficient. Images in the test datasets were segmented by the trained segmentation network, and the segmentation results were saved in binary images. Then the extremum points and corner points were detected by calling the function in the OpenCV library to obtain the coordinates of the four landmarks of the sella turcica. Finally, the length, diameter, and depth of the sella turcica can be obtained by calculating the distance between the two points and the distance from the point to the straight line. Meanwhile, images were measured manually using Digimizer. Intraclass correlation coefficients (ICCs) and Bland-Altman plots were used to analyze the consistency between automatic and manual measurements to evaluate the reliability of the proposed methodology. RESULTS The dice coefficient of the segmentation network is 92.84%. For the measurement of sella turcica, there is excellent agreement between the automatic measurement and the manual measurement. In Test1, the ICCs of length, diameter and depth are 0.954, 0.953, and 0.912, respectively. In Test2, ICCs of length, diameter and depth are 0.906, 0.921, and 0.915, respectively. In addition, Bland-Altman plots showed the excellent reliability of the automated measurement method, with the majority measurements differences falling within ± 1.96 SDs intervals around the mean difference and no bias was apparent. CONCLUSIONS Our experimental results indicated that the proposed methodology could complete the automatic segmentation of the sella turcica efficiently, and reliably predict the length, diameter, and depth of the sella turcica. Moreover, the proposed method has generalization ability according to its excellent performance on Test2.
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Affiliation(s)
- Qi Feng
- College of Medicine, Guizhou University, Guiyang, 550025, China
| | - Shu Liu
- Department of Orthodontics, Guiyang Hospital of Stomatology, Guiyang, 550002, China
| | - Ju-Xiang Peng
- Department of Orthodontics, Guiyang Hospital of Stomatology, Guiyang, 550002, China
| | - Ting Yan
- Department of Radiology, Guiyang Hospital of Stomatology, Guiyang, 550002, China
| | - Hong Zhu
- Department of Medical Information, Guiyang Hospital of Stomatology, Guiyang, 550002, China
| | - Zhi-Jun Zheng
- Department of Orthodontics, Guiyang Hospital of Stomatology, Guiyang, 550002, China
| | - Hong-Chao Feng
- College of Medicine, Guizhou University, Guiyang, 550025, China.
- Department of Oral and Maxillofacial Surgery, Guiyang Hospital of Stomatology, Guiyang, 550002, China.
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Lootus M, Beatson L, Atwood L, Bourdais T, Steyaert S, Sarabu C, Framroze Z, Dickinson H, Steels JC, Lewis E, Shah NR, Rinaldo F. Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones. Digit Biomark 2023; 7:63-73. [PMID: 37545566 PMCID: PMC10399113 DOI: 10.1159/000531224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 05/16/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones. Methods A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming "selfie" videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model's performance for MRD1 estimation was evaluated on a separate test fold from the study dataset. Results On the full test fold (N = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (r = 0.732). The mean absolute error was 0.822 mm; the mean of differences was -0.256 mm; and 95% limits of agreement (LOA) were -0.214-1.768 mm. Model performance showed no improvement when test data were gated to exclude "poor" quality images. Conclusions On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (r = 0.732) between ground truth and predicted MRD1.
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Affiliation(s)
| | | | | | | | - Sandra Steyaert
- Stanford University, Center for Bioinformatics Research, Palo Alto, CA, USA
| | | | | | | | | | | | - Nirav R. Shah
- Clinical Excellence Research Center, Stanford University, Palo Alto, CA, USA
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A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning. OPHTHALMOLOGY SCIENCE 2022; 2:100169. [PMID: 36245755 PMCID: PMC9560561 DOI: 10.1016/j.xops.2022.100169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/02/2022] [Accepted: 05/09/2022] [Indexed: 11/22/2022]
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Lou L, Sun Y, Huang X, Jin K, Tang X, Xu Z, Zhang Q, Wang Y, Ye J. Automated Measurement of Ocular Movements Using Deep Learning-Based Image Analysis. Curr Eye Res 2022; 47:1346-1353. [PMID: 35899319 DOI: 10.1080/02713683.2022.2053165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
PURPOSE Clinical assessment of ocular movements is essential for the diagnosis and management of ocular motility disorders. This study aimed to propose a deep learning-based image analysis to automatically measure ocular movements based on photographs and to investigate the relationship between ocular movements and age. METHODS 207 healthy volunteers (414 eyes) aged 5-60 years were enrolled in this study. Photographs were taken in the cardinal gaze positions. Ocular movements were manually measured based on a modified limbus test using ImageJ and automatically measured by our deep learning-based image analysis. Correlation analyses and Bland-Altman analyses were conducted to assess the agreement between manual and automated measurements. The relationship between ocular movements and age were analyzed using generalized estimating equations. RESULTS The intraclass correlation coefficients between manual and automated measurements of six extraocular muscles ranged from 0.802 to 0.848 (P < 0.001), and the bias ranged from -0.63 mm to 0.71 mm. The average measurements were 8.62 ± 1.07 mm for superior rectus, 7.77 ± 1.24 mm for inferior oblique, 6.99 ± 1.23 mm for lateral rectus, 6.71 ± 1.22 mm for medial rectus, 6.81 ± 1.20 mm for inferior rectus, and 6.63 ± 1.37 mm for superior oblique, respectively. Ocular movements in each cardinal gaze position were negatively related to age (P < 0.05). CONCLUSIONS The automated measurements of ocular movements using a deep learning-based approach were in excellent agreement with the manual measurements. This new approach allows objective assessment of ocular movements and shows great potential in the diagnosis and management of ocular motility disorders.
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Affiliation(s)
- Lixia Lou
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Yiming Sun
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Xingru Huang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, The United Kingdom
| | - Kai Jin
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Xiajing Tang
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Zhaoyang Xu
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, The United Kingdom
| | - Qianni Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, The United Kingdom
| | - Yaqi Wang
- College of Media Engineering, Communication University of Zhejiang, Hangzhou, China
| | - Juan Ye
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
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