Animal model for in-vivo Nuclear Cataract. Lens hardness and elasticity assessment.

Age-related cataract is the most frequent cause of blindness in the world being responsible for 48% of blindness and affecting more than 10% of the working population. Currently there is no objective data of the lens biomechanical properties so the process by which the cataract affects the lens's properties (e.g. hardness and elasticity) is still unclear. A modified animal model was produced to create different severities of nuclear cataract. Different doses of sodium selenite were injected in two different moments of the rat' eyes maturation resulting in 12, 13 and 11 rats with incipient, moderate and severe cataract, respectively. The nucleus and cortex's hardness and the stiffness were measured using NanoTest™. Statistically significant differences were found between healthy and cataractous lenses. Statistically significant differences were also found between the different nuclear cataract degrees (p = 0.016), showing that the lens' hardness increases with cataract formation. The nucleus shows a higher hardness increase with cataract formation (p = 0.049). The animal model used in this study allowed for the first time the characterization of the lens's hardness and elasticity in two regions of the lens, in healthy and cataractous lenses.

[Adaptation of the Standardized Patient Evaluation of Eye Dryness Questionnaire to European Portuguese (SPEED-Vp) in a Non-Clinical Sample]. / Adaptação do Questionário Standardized Patient Evaluation of Eye Dryness para Português (SPEED-Vp) numa População Não Clínica.

INTRODUCTION: The aim of this study was to translate and adapt the Standardized Patient Evaluation of Eye Dryness questionnaire to European Portuguese, as well as assess the psychometric performance of the translated version, including repeatability and agreement. MATERIAL AND METHODS: The original Standardized Patient Evaluation of Eye Dryness - SPEED questionnaire was translated and adapted to the Portuguese cultural context by following a scientifically valid methodology commonly used in the process of adapting tools to other cultures and languages. The questionnaire resulting from the translation into the new language was subject to a pre-test where the comments of the participants were written and considered for the final version of the questionnaire. For the scale validation of the final version of the translated questionnaire, 89 subjects from a non-clinical population, aged 18 to 84 years, were asked to answer the questionnaire (61% were women). One week later, the same questionnaire was repeated by 63 subjects. The internal reliability of the questionnaire was analyzed by Cronbach's alpha, temporal stability by test-retest, and analysis of agreement between measures by the Bland-Altman method. RESULTS: The internal consistency of the translated questionnaire, SPEED-vP was high (α = 0.871) and all questionnaire items contributed to an increase in this index. This consistency was also confirmed to be high in the retest (α = 0.856) and when the sample was stratified by age and sex. The SPEED-complete questionnaire also showed high consistency (α = 0.88). The repeatability of the instrument was high (ICC 0.933; 95% CI: 0.899 and 0.960) and the Bland-Altman plot revealed good agreement between measures. CONCLUSION: The Standardized Patient Evaluation of Eye Dryness in Portuguese (SPEED-vP) showed good psychometric properties for the Portuguese population. Therefore, the translated version of the SPEED-vP questionnaire could be used to quantitatively measure the presence of dry eye symptoms in the Portuguese population.

Introdução: O objetivo deste estudo foi traduzir e adaptar o questionário de avaliação padronizada do paciente com secura ocular para a língua portuguesa, bem como avaliar o desempenho psicométrico da escala da versão traduzida, incluindo a sua repetibilidade e concordância entre medidas. Material e Métodos: O questionário original Standardized Patient Evaluation of Eye Dryness ­ SPEED foi traduzido e adaptado à cultura portuguesa, seguindo uma metodologia cientificamente válida e habitualmente utilizada no processo de adaptação de ferramentas a outras culturas e línguas. O questionário resultante da tradução para a nova língua foi sujeito a um pré-teste onde se registaram os comentários dos participantes e estes foram considerados para a versão final do questionário. Para a validação da escala da versão final do questionário traduzido participaram 89 indivíduos de uma população não clínica, com idades compreendidas entre os 18 e os 84 anos, dos quais 61% eram mulheres. Uma semana depois, o mesmo questionário foi preenchido pela segunda vez por 63 indivíduos. A confiabilidade interna do questionário foi analisada pelo alfa de Cronbach, a estabilidade temporal pelo teste-reteste e a análise da concordância entre medidas pelo método Bland-Altman. Resultados: A consistência interna do questionário traduzido, SPEED-vP, foi alta (α = 0,871) e todos os itens do questionário contribuíram para um aumento deste índice. Esta consistência confirmou-se também alta no reteste (α = 0,856) e quando a amostra foi estratificada por idades e por sexo. O questionário SPEED-completo também apresentou alta consistência (α = 0,88). A repetibilidade do instrumento foi alta (ICC 0,933; 95% IC: 0,899 e 0,960) e o gráfico de Bland-Altman revela boa concordância entre medidas. Conclusão: O questionário Standardized Patient Evaluation of Eye Dryness, na língua portuguesa (SPEED-vP) demonstrou boas propriedades psicométricas na população portuguesa. Consequentemente, a versão traduzida do questionário SPEED poderá ser usada para medir quantitativamente a presença de sintomas de olho seco, na população portuguesa.

Feasibility assessment of the Eye Scan Ultrasound System for cataract characterization and optimal phacoemulsification energy estimation: protocol for a pilot, nonblinded and monocentre study.

BACKGROUND: Cataracts are lens opacifications that are responsible for more than half of blindness cases worldwide, and the only treatment is surgical intervention. Phacoemulsification surgery, the most frequently performed cataract surgery in developed countries, has associated risks, some of which are related to excessive phacoemulsification energy levels and times. The protocol proposed in herein will be used to evaluate the feasibility of a new experimental medical device, the Eye Scan Ultrasound System (ESUS), for the automatic classification of cataract type and severity and quantitative estimation of the optimal phacoemulsification energy. METHODS: The pilot study protocol will be used to evaluate the feasibility and safety of the ESUS in clinical practice. The study will be conducted in subjects with age-related cataracts and on healthy subjects as controls. The procedures include data acquisition with the experimental ESUS, classification based on the Lens Opacity Classification System III (LOCS III, comparator) using a slit lamp, contrast sensitivity test, optical coherence tomography, specular microscopy and surgical parameters. ESUS works in A-scan pulse-echo mode, with a central frequency of 20 MHz. From the collected signals, acoustic parameters will be extracted and used for automatic cataract characterization and optimal phacoemulsification energy estimation. The study includes two phases. The data collected in the first phase (40 patients, 2 eyes per patient) will be used to train the ESUS algorithms, while the data collected in the second phase (10 patients, 2 eyes per patient) will be used to assess the classification performance. System safety will be monitored during the study. DISCUSSION: The present pilot study protocol will evaluate the feasibility and safety of the ESUS for use in clinical practice, and the results will support a larger clinical study for the efficacy assessment of the ESUS as a diagnostic tool. Ultimately, the ESUS is expected to represent a valuable tool for surgical planning by reducing complications associated with excessive levels of phacoemulsification energy and surgical times, which will have a positive impact on healthcare systems and society. The study is not yet recruiting. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT04461912 , registered on July 8, 2020.

A-scan ultrasound in ophthalmology: A simulation tool.

In the present study, we developed a computational tool for simulating the ophthalmological applications of A-scan ultrasound, including cataract characterisation and biometry. A-scan biometry is used to measure the axial length (AL) of the eye before cataract surgery to calculate the refractive power of the intraocular lens to be implanted. Errors in the measurement of the AL lead to post-surgical refractive errors. The simulation tool was developed using the k-Wave Matlab toolbox, together with a user-friendly interface developed in Matlab. Diverse error sources were evaluated. Constant ultrasound speed assumptions may introduce refractive errors of up to 0.6 D; by contrast, probe positioning errors had a lower impact, of up to 0.11 D. The correct identification of the Bruch's membrane is limited not only by axial resolution constraints but also by the low reflection coefficient at the retina/choroid interface. Regarding cataract characterisation, the amplitudes of the echoes reflected at the lens interfaces are sensitive to diverse cataract types and severities, and a more realistic representation could be obtained by using a higher resolution in the eye grid; however, the required computational times would make simulations impracticable when using personal computers. The simulation tool shows good versatility for evaluating diverse aspects of A-scan biometry.

Safety Assessment of an A-Scan Ultrasonic System for Ophthalmic Use.

OBJECTIVES: This study describes the safety assessment of an A-scan ultrasonic system for ophthalmic use. The system is an investigational medical device for automatic cataract detection and classification. METHODS: The risk management was based on the International Organization for Standardization (ISO) standard DIN EN ISO 14971:2009-10 and International Electrotechnical Commission (IEC) standard IEC 60601-2-37. The calibration of the ultrasonic field was conducted according to the standards IEC 62127-1:2007 and IEC 62359:2010. The uncertainty on measurements was delineated in agreement with the guide JCGM 100:2008. RESULTS: After risk management, all risks were qualitatively classified as acceptable. The mechanical index (0.08 ± 0.05), soft tissue thermal index (0.08 ± 0.08) and spatial-peak temporal-average intensity (0.56 ± 0.59 mW/cm2 ) were under the maximum index values indicated by the US Food and Drug Administration guidance, Marketing Clearance of Diagnostic Ultrasound Systems and Transducers (0.23, 1, and 17 mW/cm2 , respectively). CONCLUSIONS: This study presents a practical approach for the safety assessment of A-scan ultrasonic systems for ophthalmic use. The safety evaluation of a medical device is mandatory before its use in clinical practice. However, the safety monitoring throughout its life cycle should also be considered, since many device components may deteriorate over time and use.

In Vivo Characterization of Corneal Changes in a Type 1 Diabetic Animal Model.

Diabetes mellitus (DM) is a metabolic disease that affects 9% of the adult population, promoting an increase in glucose concentration that affects the corneal structure, namely, its thickness, as well as the constituents and flow of the aqueous humor. In this study, high-frequency transducers (20-MHz and 50-MHz) were used to measure and characterize changes in the corneal and aqueous humor in streptozotocin-induced type 1 diabetic rats followed over 8 weeks. Increases of 24.6 and 15.4 µm in central corneal thickness were measured with the 20-MHz and 50-MHz probes, respectively, in DM rats (p < 0.001). The increases in thickness of the different corneal layers ranged from 7% to 17%. Structural alterations of the aqueous humor were also studied by relating the amplitudes of the anterior lens and posterior cornea boundary signals, the result of which was denominated by pseudo-attenuation. The results revealed an increase of 49% at week 8 compared with the baseline values (p < 0.020, with the 50-MHz probe). This study illustrated that high-frequency ultrasound can be used to measure corneal layer thickness and study the alterations promoted by diabetes in the eye's anterior segment. Those assessments may allow early detection of DM, improving the monitoring of diabetic patients.

Machine Learning Techniques in Clinical Vision Sciences.

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.

In-Vivo Automatic Nuclear Cataract Detection and Classification in an Animal Model by Ultrasounds.

OBJECTIVE: To early detect nuclear cataract in vivo and automatically classify its severity degree, based on the ultrasound technique, using machine learning. METHODS: A 20-MHz ophthalmic ultrasound probe with a focal length of 8.9 mm and an active diameter of 3 mm was used. Twenty-seven features in time and frequency domain were extracted for cataract detection and classification with support vector machine (SVM), Bayes, multilayer perceptron, and random forest classifiers. Fifty rats were used: 14 as control and 36 as study group. An animal model for nuclear cataract was developed. Twelve rats with incipient, 13 with moderate, and 11 with severe cataract were obtained. The hardness of the nucleus and the cortex regions was objectively measured in 12 rats using the NanoTest. RESULTS: Velocity, attenuation, and frequency downshift significantly increased with cataract formation ( ). The SVM classifier showed the higher performance for the automatic classification of cataract severity, with a precision, sensitivity, and specificity of 99.7% (relative absolute error of 0.4%). A statistically significant difference was found for the hardness of the different cataract degrees ( P = 0.016). The nucleus showed a higher hardness increase with cataract formation ( P = 0.049 ). A moderate-to-good correlation between the features and the nucleus hardness was found in 23 out of the 27 features. CONCLUSION: The developed methodology made possible detecting the nuclear cataract in-vivo in early stages, classifying automatically its severity degree and estimating its hardness. SIGNIFICANCE: Based on this work, a medical prototype will be developed for early cataract detection, classification, and hardness estimation.

Automatic Cataract Hardness Classification Ex Vivo by Ultrasound Techniques.

To demonstrate the feasibility of a new methodology for cataract hardness characterization and automatic classification using ultrasound techniques, different cataract degrees were induced in 210 porcine lenses. A 25-MHz ultrasound transducer was used to obtain acoustical parameters (velocity and attenuation) and backscattering signals. B-Scan and parametric Nakagami images were constructed. Ninety-seven parameters were extracted and subjected to a Principal Component Analysis. Bayes, K-Nearest-Neighbours, Fisher Linear Discriminant and Support Vector Machine (SVM) classifiers were used to automatically classify the different cataract severities. Statistically significant increases with cataract formation were found for velocity, attenuation, mean brightness intensity of the B-Scan images and mean Nakagami m parameter (p < 0.01). The four classifiers showed a good performance for healthy versus cataractous lenses (F-measure ≥ 92.68%), while for initial versus severe cataracts the SVM classifier showed the higher performance (90.62%). The results showed that ultrasound techniques can be used for non-invasive cataract hardness characterization and automatic classification.

Using ultrasound backscattering signals and Nakagami statistical distribution to assess regional cataract hardness.

This study aims to analyze the protein aggregates spatial distribution for different cataract degrees, and correlate this information with the lens acoustical parameters and by this way, assess the cataract regional hardness. Different cataract degrees were induced ex vivo in porcine lenses. A 25 MHz ultrasonic transducer was used to obtain the acoustical parameters (velocity, attenuation, and backscattering signals). B-scan and Nakagami images were constructed. Also, lenses with different cataract degrees were sliced in two regions (nucleus and cortex), for fibers and collagen detection. A significant increase with cataract formation was found for the velocity, attenuation, and brightness intensity of the B-scan images and Nakagami m parameter ( ). The acoustical parameters showed a good to moderate correlation with the m parameter for the different stages of cataract formation. A strong correlation was found between the protein aggregates in the cortex and the m parameter. Lenses without cataract are characterized using a classification and regression tree, by a mean brightness intensity ≤0.351, a variance of the B-scan brightness intensity ≤0.070, a velocity ≤1625 m/s, and an attenuation ≤0.415 dB/mm·MHz (sensitivity: 100% and specificity: 72.6%). To characterize different cataract degrees, the m parameter should be considered. Initial stages of cataract are characterized by a mean brightness intensity >0.351 and a variance of the m parameter >0.110. Advanced stages of cataract are characterized by a mean brightness intensity >0.351, a variance of the m parameter ≤0.110, and a mean m parameter >0.374. For initial and advanced stages of cataract, a sensitivity of 78.4% and a specificity of 86.5% are obtained.