Predicting an unstable tear film through artificial intelligence

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Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper presents an exhaustive study about the problem at hand using different texture analysis methods in three colour spaces and different machine learning algorithms. All these methods and classifiers have been tested on a dataset composed of 105 images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated with the benefits of being faster and unaffected by subjective factors, with maximum accuracy over 95%

Artificial intelligence in dry eye disease

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation

Gaya belajar pelajar tingkatan lima aliran perdagangan di lima buah sekolah menengah teknik : suatu kajian

Kajian ini mengenal pasti gaya belajar Visual, Auditori dan Kinestetik pelajar tingkatan lima aliran perdagangan di lima buah sekolah menengah teknik. Objektif utama kajian ini adalah untuk mengenal pasti peratus setiap kecenderungan gaya belajar di setiap sekolah yang dikaji. Satu ratus lima puluh orang pelajar telah dijadikan responden kajian. Kajian ini adalah berbentuk deskriptif dan instrumen pengumpulan data yang digunakan adalah borang soal selidik yang telah diubahsuai dari Learning Style Inventory. Data dianalisis menggunakan Statistical Package for Social Sciences (SPSS) versi 11.5 bagi mendapatkan peratus, min, sisihan piawai serta mengenal pasti sekiranya terdapat perbezaan yang signifikan setiap skor gaya belajar di kalangan sekolah yang dikaji. Dapatan kajian menunjukkan peratus kecenderungan gaya pelajar paling tinggi ialah gaya belajar Auditori diikuti dengan gaya belajar Kinestetik dan Visual

Advancing the diagnosis of dry eye syndrome : development of automated assessments of tear film lipid layer patterns

[Resumen] El síndrome de ojo seco es una enfermedad sintomática que afecta a un amplio rango de la población, y tiene un impacto negativo en sus actividades diarias. Su diagnóstico es una tarea difícil debido a su etiología multifactorial, y por eso existen varias pruebas clínicas. Una de esas pruebas es la evaluación de los patrones interferenciales de la capa lipídica de la película lagrimal. Guillon dise˜nó un instrumento denominado Tearscope Plus para evaluar el grosor de la película lagrimal de forma rápida, y también definió una escala de clasificación compuesta de cinco categorías. La clasificación en uno de esos cinco patrones es una tarea clínica dificil, especialmente con las capas lipídicas más finas que carecen de características de color y/o morfológicas. Además, la interpretación subjetiva de los expertos mediante una revisión visual puede afectar a la clasificación, pudiendo producirse un alto grado de inter- e intra- variabilidad entre observadores. El desarrollo de un método sistemático y objetivo para análisis y clasificación es altamente deseable, permitiendo un diagnóstico homogéneo y liberando a los expertos de esta tediosa tarea. La propuesta de esta investigación es el diseño de un sistema automático para evaluar los patrones de la capa lipídica de la película lagrimal mediante la interpretación de las imágenes obtenidas con el Tearscope Plus. Por una parte, se presenta una metodología global para evaluar la capa lipídica de la película lagrimal mediante la clasificación automática de estas imágenes en una de las categorías de Guillon. El proceso se lleva a cabo mediante el uso de modelos de textura y color, y algoritmos de aprendizaje máquina. A continuación, esta metodología global se optimiza mediante la reducción de su complejidad computacional. Se utilizan técnicas de reducción de la dimensión para disminuir los requisitos de memoria/tiempo sin una degradación en su rendimiento. Por otra parte, se presenta una metodología local para crear mapas de la película lagrimal, que representan la distribución local de los patrones de la capa lipídica sobre la película lagrimal. Las diferentes evaluaciones automáticas que se proponen ahorran tiempo a los expertos, y proporcionan resultados imparciales que no están afectados por factores subjetivos.[Resumo] O síndrome de ollo seco é unha enfermidade sintomática que afecta a un amplo rango da poboación, e ten un impacto negativo nas súas actividades diarias. O seu diagnóstico é unha tarefa difícil debido á súa etioloxía multifactorial, e por iso existen varias probas clínicas. Unha desas probas é a avaliación dos patróns interferenciais da capa lipídica da película lagrimal. Guillon dese˜nou un instrumento denominado Tearscope Plus para avaliar o grosor da película lagrimal de forma rápida, e tamén definiu unha escala de clasificación composta de cinco categorías. A clasificación nun deses cinco patróns é unha tarefa clínica difícil, especialmente coas capas lipídicas máis finas que carecen de características de cor e/ou morfolóxicas. Ademais, a interpretación subxectiva dos expertos mediante una revisión visual pode afectar á clasificación, podendo producirse un alto grao de inter- e intra- variabilidade entre observadores. O desenvolvemento dun método sistemático e obxectivo para análise e clasificación é altamente desexable, permitindo un diagnóstico homoxéneo e liberando aos expertos desta tediosa tarefa. A proposta desta investigación é o deseño dun sistema automático para avaliar os patróns da capa lipídica da película lagrimal mediante a interpretación das imaxes obtidas co Tearscope Plus. Por unha parte, preséntase unha metodoloxía global para avaliar a capa lipídica da película lagrimal mediante a clasificación automática destas imaxes nunha das categorías de Guillon. O proceso é levado a cabo mediante o uso de modelos de textura e cor, e algoritmos de aprendizaxe máquina. A continuación, esta metodoloxía global é optimizada mediante a redución da súa complexidade computacional. Utilízanse técnicas de redución da dimensión para diminuír os requisitos de memoria/tempo sen unha degradación no seu rendemento. Por outra parte, preséntase unha metodoloxía local para crear mapas da película lagrimal, que representan a distribución local dos patróns da capa lipídica sobre a película lagrimal. As diferentes avaliacións automáticas que se propoñen aforran tempo aos expertos, e proporcionan resultados imparciais que non están afectados por factores subxectivos.[Abstract] Dry eye syndrome is a symptomatic disease which affects a wide range of population, and has a negative impact on their daily activities. Its diagnosis is a difficult task due to its multifactorial etiology, and so there exist several clinical tests. One of these tests is the evaluation of the interference patterns of the tear film lipid layer. Guillon designed an instrument known as Tearscope Plus which allows clinicians to rapidly assess the lipid layer thickness, and also defined a grading scale composed of five categories. The classification into these five patterns is a difficult clinical task, especially with thinner lipid layers which lack color and/or morphological features. Furthermore, the subjective interpretation of the experts via visual inspection may affect the classification, and so a high degree of inter- and also intra- observer variability can be produced. The development of a systematic, objective computerized method for analysis and classification is thus highly desirable, allowing for homogeneous diagnosis and relieving the experts from this tedious task. The proposal of this research is the design of an automatic system to assess the tear film lipid layer patterns through the interpretation of the images acquired with the Tearscope Plus. On the one hand, a global methodology is presented to assess the tear film lipid layer by automatically classifying these images into the Guillon categories. The process is carried out using texture and color models, and machine learning algorithms. Then, this global methodology is optimized through the reduction of its computational complexity. Dimensionality reduction techniques are used in order to diminish the memory/time requirements with no degradation in performance. On the other hand, a local methodology is also presented to create tear film maps, which represent the local distribution of the lipid layer patterns over the tear film. The different automated assessments proposed save time for experts, and provide unbiased results which are not affected by subjective factors

Development of Novel Diagnostic Tools for Dry Eye Disease using Infrared Meibography and In Vivo Confocal Microscopy

Dry eye disease (DED) is a multifactorial disease of the ocular surface where tear film instability, hyperosmolarity, neurosensory abnormalities, meibomian gland dysfunction, ocular surface inflammation and damage play a dedicated etiological role. Estimated 5 to 50% of the world population in different demographic locations, age and gender are currently affected by DED. The risk and occurrence of DED increases at a significant rate with age, which makes dry eye a major growing public health issue. DED not only impacts the patient’s quality of vision and life, but also creates a socio-economic burden of millions of euros per year. DED diagnosis and monitoring can be a challenging task in clinical practice due to the multifactorial nature and the poor correlation between signs and symptoms. Key clinical diagnostic tests and techniques for DED diagnosis include tearfilm break up time, tear secretion – Schirmer’s test, ocular surface staining, measurement of osmolarity, conjunctival impression cytology. However, these clinical diagnostic techniques are subjective, selective, require contact, and are unpleasant for the patient’s eye. Currently, new advances in different state-of-the-art imaging modalities provide non-invasive, non- or semi-contact, and objective parameters that enable objective evaluation of DED diagnosis. Among the different and constantly evolving imaging modalities, some techniques are developed to assess morphology and function of meibomian glands, and microanatomy and alteration of the different ocular surface tissues such as corneal nerves, immune cells, microneuromas, and conjunctival blood vessels. These clinical parameters cannot be measured by conventional clinical assessment alone. The combination of these imaging modalities with clinical feedback provides unparalleled quantification information of the dynamic properties and functional parameters of different ocular surface tissues. Moreover, image-based biomarkers provide objective, specific, and non / marginal contact diagnosis, which is faster and less unpleasant to the patient’s eye than the clinical assessment techniques. The aim of this PhD thesis was to introduced deep learning-based novel computational methods to segment and quantify meibomian glands (both upper and lower eyelids), corneal nerves, and dendritic cells. The developed methods used raw images, directly export from the clinical devices without any image pre-processing to generate segmentation masks. Afterward, it provides fully automatic morphometric quantification parameters for more reliable disease diagnosis. Noteworthily, the developed methods provide complete segmentation and quantification information for faster disease characterization. Thus, the developed methods are the first methods (especially for meibomian gland and dendritic cells) to provide complete morphometric analysis. Taken together, we have developed deep learning based automatic system to segment and quantify different ocular surface tissues related to DED namely, meibomian gland, corneal nerves, and dendritic cells to provide reliable and faster disease characterization. The developed system overcomes the current limitations of subjective image analysis and enables precise, accurate, reliable, and reproducible ocular surface tissue analysis. These systems have the potential to make an impact clinically and in the research environment by specifying faster disease diagnosis, facilitating new drug development, and standardizing clinical trials. Moreover, it will allow both researcher and clinicians to analyze meibomian glands, corneal nerves, and dendritic cells more reliably while reducing the time needed to analyze patient images significantly. Finally, the methods developed in this research significantly increase the efficiency of evaluating clinical images, thereby supporting and potentially improving diagnosis and treatment of ocular surface disease

A review of artificial intelligence applications in anterior segment ocular diseases

Background: Artificial intelligence (AI) has great potential for interpreting and analyzing images and processing large amounts of data. There is a growing interest in investigating the applications of AI in anterior segment ocular diseases. This narrative review aims to assess the use of different AI-based algorithms for diagnosing and managing anterior segment entities. Methods: We reviewed the applications of different AI-based algorithms in the diagnosis and management of anterior segment entities, including keratoconus, corneal dystrophy, corneal grafts, corneal transplantation, refractive surgery, pterygium, infectious keratitis, cataracts, and disorders of the corneal nerves, conjunctiva, tear film, anterior chamber angle, and iris. The English-language databases PubMed/MEDLINE, Scopus, and Google Scholar were searched using the following keywords: artificial intelligence, deep learning, machine learning, neural network, anterior eye segment diseases, corneal disease, keratoconus, dry eye, refractive surgery, pterygium, infectious keratitis, anterior chamber, and cataract. Relevant articles were compared based on the use of AI models in the diagnosis and treatment of anterior segment diseases. Furthermore, we prepared a summary of the diagnostic performance of the AI-based methods for anterior segment ocular entities. Results: Various AI methods based on deep and machine learning can analyze data obtained from corneal imaging modalities with acceptable diagnostic performance. Currently, complicated and time-consuming manual methods are available for diagnosing and treating eye diseases. However, AI methods could save time and prevent vision impairment in eyes with anterior segment diseases. Because many anterior segment diseases can cause irreversible complications and even vision loss, sufficient confidence in the results obtained from the designed model is crucial for decision-making by experts. Conclusions: AI-based models could be used as surrogates for analyzing manual data with improveddiagnostic performance. These methods could be reliable tools for diagnosing and managing anterior segmentocular diseases in the near future in remote areas. It is expected that future studies can design algorithms thatuse less data in a multitasking manner for the detection and management of anterior segment diseases

Fitting ODE models of tear film breakup

Several elements are developed to quantitatively determine the contribution of different physical and chemical effects to tear breakup (TBU) in normal subjects. Fluorescence (FL) imaging is employed to visualize the tear film and to determine tear film (TF) thinning and potential TBU. An automated system using a convolutional neural network was trained and deployed to identify multiple TBU instances in each trial. Once identified, extracted FL intensity data was fit by mathematical models that included tangential flow along the eye, evaporation, osmosis and FL intensity of emission from the tear film. Optimizing the fit of the models to the FL intensity data determined the mechanism(s) driving each instance of TBU and produced an estimate of the osmolarity within TBU. Initial estimates for FL concentration and initial TF thickness agree well with prior results. Fits were produced for $N=467$ instances of potential TBU from 15 normal subjects. The results showed a distribution of causes of TBU in these normal subjects, as reflected by estimated flow and evaporation rates, which appear to agree well with previously published data. Final osmolarity depended strongly on the TBU mechanism, generally increasing with evaporation rate but complicated by the dependence on flow. The method has the potential to classify TBU instances based on the mechanism and dynamics and to estimate the final osmolarity at the TBU locus. The results suggest that it might be possible to classify individual subjects and provide a baseline for comparison and potential classification of dry eye disease subjects