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Bioengineering
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Ophthalmic Engineering 2.0
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Ophthalmic Engineering 2.0

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Regenerative Engineering".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2119

Special Issue Editor

Prof. Dr. Xiaofei Wang

E-Mail Website
Guest Editor
School of Biological Science and Medical Engineering, Beihang University, Beijing, China
Interests: biomechanics; ophthalmology; image processing; artificial intelligence; eye movement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The majority of people rely on their eyes to perceive and make sense of the world. However, the eye and the visual system are vulnerable to diseases and disorders at every stage of life. At present, at least 2.2 billion people around the world have a vision impairment, according to the World Report On Vision by WHO.

Recent advances in bioengineering are conveying exciting changes to the field of ophthalmology and visual science. Engineering methods such as biomechanics, novel imaging modalities, tissue engineering, virtual reality and artificial intelligence have exhibited great success with regard to diagnosing, treating and understanding the mechanisms of various eye diseases.

In this Special Issue, we will focus on the vast range of potential bioengineering methods to be applied and their applications in ophthalmology and visual science. Both original research contributions and review papers are welcome. Topics may include, but are not limited to, the following:

  • ocular biomechanics;
  • ophthalmic imaging;
  • artificial intelligence in ophthalmology;
  • virtual reality;
  • biomolecular, cellular and tissue engineering in ophthalmology;
  • novel diagnostic and treatment methods in ophthalmology;
  • material characterization of ocular tissue.

Prof. Dr. Xiaofei Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ophthalmology
  • biomechanics
  • eye
  • medical imaging
  • optical coherence tomography
  • artificial intelligence
  • deep learning
  • virtual reality
  • medical device
  • finite element method
  • inverse analysis

Published Papers (3 papers)

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13 pages, 1676 KiB  
Article
Diagnosis of Forme Fruste Keratoconus Using Corvis ST Sequences with Digital Image Correlation and Machine Learning
by Lanting Yang, Kehan Qi, Peipei Zhang, Jiaxuan Cheng, Hera Soha, Yun Jin, Haochen Ci, Xianling Zheng, Bo Wang, Yue Mei, Shihao Chen and Junjie Wang
Bioengineering 2024, 11(5), 429; https://doi.org/10.3390/bioengineering11050429 - 26 Apr 2024
Viewed by 446
Abstract
Purpose: This study aimed to employ the incremental digital image correlation (DIC) method to obtain displacement and strain field data of the cornea from Corvis ST (CVS) sequences and access the performance of embedding these biomechanical data with machine learning models to distinguish [...] Read more.
Purpose: This study aimed to employ the incremental digital image correlation (DIC) method to obtain displacement and strain field data of the cornea from Corvis ST (CVS) sequences and access the performance of embedding these biomechanical data with machine learning models to distinguish forme fruste keratoconus (FFKC) from normal corneas. Methods: 100 subjects were categorized into normal (N = 50) and FFKC (N = 50) groups. Image sequences depicting the horizontal cross-section of the human cornea under air puff were captured using the Corvis ST tonometer. The high-speed evolution of full-field corneal displacement, strain, velocity, and strain rate was reconstructed utilizing the incremental DIC approach. Maximum (max-) and average (ave-) values of full-field displacement V, shear strain γxy, velocity VR, and shear strain rate γxyR were determined over time, generating eight evolution curves denoting max-V, max-γxy, max-VR, max-γxyR, ave-V, ave-γxy, ave-VR, and ave-γxyR, respectively. These evolution data were inputted into two machine learning (ML) models, specifically Naïve Bayes (NB) and Random Forest (RF) models, which were subsequently employed to construct a voting classifier. The performance of the models in diagnosing FFKC from normal corneas was compared to existing CVS parameters. Results: The Normal group and the FFKC group each included 50 eyes. The FFKC group did not differ from healthy controls for age (p = 0.26) and gender (p = 0.36) at baseline, but they had significantly lower bIOP (p < 0.001) and thinner central cornea thickness (CCT) (p < 0.001). The results demonstrated that the proposed voting ensemble model yielded the highest performance with an AUC of 1.00, followed by the RF model with an AUC of 0.99. Radius and A2 Time emerged as the best-performing CVS parameters with AUC values of 0.948 and 0.938, respectively. Nonetheless, no existing Corvis ST parameters outperformed the ML models. A progressive enhancement in performance of the ML models was observed with incremental time points during the corneal deformation. Conclusion: This study represents the first instance where displacement and strain data following incremental DIC analysis of Corvis ST images were integrated with machine learning models to effectively differentiate FFKC corneas from normal ones, achieving superior accuracy compared to existing CVS parameters. Considering biomechanical responses of the inner cornea and their temporal pattern changes may significantly improve the early detection of keratoconus. Full article
(This article belongs to the Special Issue Ophthalmic Engineering 2.0)
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10 pages, 1143 KiB  
Article
Longitudinal Analysis of Corneal Biomechanics of Suspect Keratoconus: A Prospective Case-Control Study
by Yan Huo, Xuan Chen, Ruisi Xie, Jing Li and Yan Wang
Bioengineering 2024, 11(5), 420; https://doi.org/10.3390/bioengineering11050420 - 25 Apr 2024
Viewed by 328
Abstract
Background: To evaluate the corneal biomechanics of stable keratoconus suspects (Stable-KCS) at 1-year follow-up and compare them with those of subclinical keratoconus (SKC). Methods: This prospective case-control study included the eyes of 144 patients. Biomechanical and tomographic parameters were recorded (Corvis ST and [...] Read more.
Background: To evaluate the corneal biomechanics of stable keratoconus suspects (Stable-KCS) at 1-year follow-up and compare them with those of subclinical keratoconus (SKC). Methods: This prospective case-control study included the eyes of 144 patients. Biomechanical and tomographic parameters were recorded (Corvis ST and Pentacam). Patients without clinical signs of keratoconus in both eyes but suspicious tomography findings were included in the Stable-KCS group (n = 72). Longitudinal follow-up was used to evaluate Stable-KCS changes. Unilateral keratoconus contralateral eyes with suspicious tomography were included in the SKC group (n = 72). T-tests and non-parametric tests were used for comparison. Multivariate general linear models were used to adjust for confounding factors for further analysis. Receiver operating characteristic (ROC) curves were used to analyze the distinguishability. Results: The biomechanical and tomographic parameters of Stable-KCS showed no progression during the follow-up time (13.19 ± 2.41 months, p > 0.05). Fifteen biomechanical parameters and the Stress–Strain Index (SSI) differed between the two groups (p < 0.016). The A1 dArc length showed the strongest distinguishing ability (area under the ROC = 0.888) between Stable-KCS and SKC, with 90.28% sensitivity and 77.78% specificity at the cut-off value of −0.0175. Conclusions: The A1 dArc length could distinguish between Stable-KCS and SKC, indicating the need to focus on changes in the A1 dArc length for keratoconus suspects during the follow-up period. Although both have abnormalities on tomography, the corneal biomechanics and SSI of Stable-KCS were stronger than those of SKC, which may explain the lack of progression of Stable-KCS. Full article
(This article belongs to the Special Issue Ophthalmic Engineering 2.0)
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12 pages, 785 KiB  
Article
Mendelian Randomisation Analysis of Causal Association between Lifestyle, Health Factors, and Keratoconus
by Jiaxuan Cheng, Lanting Yang, Yishan Ye, Lvfu He, Shihao Chen and Junjie Wang
Bioengineering 2024, 11(3), 221; https://doi.org/10.3390/bioengineering11030221 - 26 Feb 2024
Viewed by 982
Abstract
Keratoconus (KC), a leading cause of vision impairment, has an unclear aetiology. This study used Mendelian randomization (MR) to explore the causal links between various factors (smoking, asthma, Down syndrome, inflammatory bowel disease, atopic dermatitis, and serum 25-hydroxyvitamin D levels) and KC. A [...] Read more.
Keratoconus (KC), a leading cause of vision impairment, has an unclear aetiology. This study used Mendelian randomization (MR) to explore the causal links between various factors (smoking, asthma, Down syndrome, inflammatory bowel disease, atopic dermatitis, and serum 25-hydroxyvitamin D levels) and KC. A two-sample MR design, grounded in genome-wide association study (GWAS) summary statistics, was adopted using data from FinnGen, UK Biobank, and other GWAS-related articles. The inverse-variance weighted (IVW) method was employed, complemented by the Wald ratio method for factors with only one single-nucleotide polymorphism (SNP). Sensitivity and stability were assessed through Cochrane’s Q test, the MR-Egger intercept test, MR-PRESSO outlier test, and the leave-one-out analysis. The IVW results for the ORA (Ocular Response Analyzer) biomechanical parameters indicated significant associations between tobacco smoking (CH: p < 0.001; CRF: p = 0.009) and inflammatory bowel disease (CH: p = 0.032; CRF: p = 0.001) and corneal biomechanics. The Wald ratio method showed tobacco smoking was associated with a lower risk of KC (p = 0.024). Conversely, asthma (p = 0.009), atopic dermatitis (p = 0.012), inflammatory bowel disease (p = 0.017), and serum 25-hydroxyvitamin D levels (p = 0.039) were associated with a higher risk of KC by IVW, and the same applied to Down syndrome (p = 0.004) using the Wald ratio. These results underscore the role of corneal biomechanics as potential mediators in KC risk, warranting further investigation using Corvis ST and Brillouin microscopy. The findings emphasise the importance of timely screening for specific populations in KC prevention and management. Full article
(This article belongs to the Special Issue Ophthalmic Engineering 2.0)
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