This thesis has been focused on improving access to eyecare through the design and development of portable medical devices that use low cost components, and the implementation of advanced algorithms that allows the detection and diagnosis of different eye diseases.
Uncorrected refractive errors (UREs) are a global issue impacting over 900 million individuals. This issue is particularly pronounced in low resource settings, where access to vision care is limited due to a shortage of eyecare professionals (ECP) capable of providing accurate eyeglass prescriptions. Although autorefraction-based prescriptions are a practical approach to addressing the global problem of UREs, there is always a measurable difference between the autorefractor measurement the clinical gold standard, the subjective refraction (SR). To try to reduce the variability between objective and SR measurements we have assessed the performance of machine learning (ML) ensemble models for predicting patient SR using demographic factors, wavefront aberrometry data, and measurement quality related metrics taken with the QuickSee aberrometer. The ML model significantly outperformed the autorefractor, reducing in a ±0.63 D, ±0.14 D, and ±0.08 D the 95% limits of agreement of the error distribution for M, J0, and J45, respectively. These results suggest that machine learning effectively enhances accuracy when predicting a subjective refraction of a patient from objective measurements acquired with a low-cost portable device.
A novel AI-based approach for the detection of manifest keratoconus from wavefront aberrometry images has been evaluated. The model consists of two components: a first convolutional neural network (CNN) trained to classify individual wavefront maps as either keratoconic positive or keratoconic negative, and a second component that leverages the dynamic nature of the QuickSee device. This second component provides the final classification label for a measurement based on the most frequent prediction among the individual predictions contained in the video. The algorithm demonstrated remarkable performance, achieving a 100% accuracy rate across training, validation, and test subgroups. This represents an advance in keratoconus detection based solely on total wavefront aberrometry data.
We have designed a new portable wavefront autorefractor, the QuickSee Free, that has been evaluated using an alternative study protocol that considers the variability of the SR. The proposed protocol compares the differences between the device and the SR performed by two independent ECPs to the differences between the SR measurements performed by the two ECPs, thus providing a measure of the device’s accuracy against the inherent variability of the SR procedure. The average difference between the device and each of the two SRs were found to be smaller than those found between the two SRs for the M, and comparable for the astigmatic components. Overall, the device had a high level of agreement with both SRs but was found to be more comparable to SR2 than to SR1. These results indicate the potential of the device to be used as a reliable tool for clinical and screening purposes.
In conclusion, this research aims to address the global problem of UREs and presents innovative solutions. These solutions include the design of the QuickSee Free device and various ML algorithms, all of which work towards improving access to eyecare, increasing the accuracy of eyeglass prescriptions, and detecting keratoconus.

El queratocono es una enfermedad ocular degenerativa que causa distorsión visual al afectar la córnea. La detección temprana resulta crucial, sin embargo, los métodos actuales son costosos e inaccesibles. En este trabajo, se propone un sistema de detección de queratocono accesible y fácil de usar basado en el análisis de imágenes mediante técnicas de aprendizaje automático.
Para capturar las imágenes, se utilizó el dispositivo QuickSee (PlenOptika Inc, MA, USA), mientras que para el análisis se emplearon redes  neuronales convolucionales. Además, se aplicó un proceso de limpieza de datos utilizando filtros diseñados con técnicas de procesamiento de imágenes para eliminar las imágenes no deseadas, lo cual resultó en la eliminación del 12% de los datos originales. En cuanto a las arquitecturas de las redes neuronales convolucionales, se adoptaron dos enfoques
diferentes: uno utilizando modelos pre-entrenados (Transfer Learning) y otro diseñado desde cero. Ambos enfoques obtuvieron una alta tasa de exactitud, superando el 95% en todos los casos, y una sensibilidad del 92.35% en el caso del modelo sin Transfer Learning, al considerar imágenes de forma individual. Además, al tomar todas las imágenes de cada paciente y quedándose con la predicción mayoritaria, se logró una tasa de acierto del 100% en las pruebas de diagnóstico de los pacientes del conjunto de test.
Estos resultados demuestran que este sistema de detección precoz del queratocono es una herramienta muy prometedora que podría implementarse en el dispositivo QuickSee en un futuro trabajo,
mejorando así la detección global de queratocono y su impacto en la salud ocular a nivel mundial.

Motivation: Uncorrected refractive errors are the main cause of visual impairment worldwide. Hundreds of millions of people suffer from uncorrected refractive errors. The great majority of this population live in low resource settings. This, however, has been proved to not be caused by difficult access to treatment, as eyeglasses are a very cost-effective treatment and the majority of all visual impairment can be treated in some way, but due to a diagnosis barrier. This is to say, there are very few professionals which can correctly diagnose these errors.
This is where the QuickSee comes in. PlenOptika Inc. (USA), in collaboration with the ‘Universidad Autónoma de Madrid’, developed the QuickSee as a solution to the global health problem caused by uncorrected refractive errors. The QuickSee is a low-cost autorefractor designed with simplicity in mind to allow personnel with minimal training to obtain an eyeglass prescription. In a few seconds, it can accurately measure existing refractive errors, thus facilitating eye care access to lower resource settings and closing the diagnosis gap.
Problem: The QuickSee is currently being fully commercialized throughout the globe. It has proved to be very useful in easing access to eyecare, as well as streamlining the process to achieve an eyeglass prescription. However, feedback received from users have shown that the refraction process may be further ‘automated’ or optimized. If subjective refraction were to be incorporated in the same step as objective refraction.

This Bachelor Thesis presents the development of a calibration system for a visual simulation device: the SimVis. This system prescribes multifocal lenses allowing patients to know if they adapt correctly to their needs before surgical implantation. To approach the project, the functionality of the calibration system has been divided into three main modules: camera, communication, and image processing, which are finally integrated into a main program. In addition, a calibration cradle prototype has been built to perform verification and validation tests.

Motivation: Worldwide, it is estimated that more than 1 billion people do not have the eyeglasses they need. This problem is especially prevalent in developing countries due to the lack of an appropriate infrastructure for eyecare deployment and eyecare professionals capable of providing eyeglass prescriptions. The procedure for obtaining these prescriptions consist of using an objective measurement of the refractive power of the eye, which is usually very accurate but can fail in several cases, followed by a subjective refinement which is performed by an eyecare professional based on the feedback of the patient. If the initial objective measurement is not accurate, it can greatly complicate the procedure increasing the time required to refract the patient and thus the bandwidth of the eyecare professionals.

Marcos Rubio Rubio

At a global level, there is a critical need for technological tools that improve access to vision care to address the most common cause of visual impairment: uncorrected refractive errors. Due to the worldwide shortage of vision care professionals,obtaining accurate prescriptions for glasses that effectively correct refractive errors and restore vision is often a challenge, especially in resource-limited settings.
This thesis describes the development of advanced ophthalmological technology capable of prescribing glasses accurately, affordably, and quickly. To achieve this, we have utilized a wavefront aberrometry-based autorefractor called QuickSee, which is portable and can provide eyeglass prescriptions in just 10 seconds. Its validation in different populations and global usage has served as the starting point for this work to further improve access to vision care worldwide. Since there is always a percentage of patients considered outliers when comparing the objective results of autorefractors to the gold standard, which is the subjective refraction process where a doctor tests and asks the patient about their comfort with different lenses, we have implemented a series of advanced features to enhance these predictions. A new algorithm has been developed that accurately simulates the patient’s vision with different corrections and facilitates the determination, through iterative calculations, of the correction that maximizes certain visual quality metrics (e.g., sharpness or contrast). Additionally, a new device, QuickSee Free Pro Keratometry, has been designed, which is portable and very low-cost, improving features such as ease of use, alignment, reduced patient cooperation in measurements, and reduced weight. This device will allow individuals with minimal training to easily obtain accurate and reliable glasses prescriptions anywhere. It will integrate a high-precision wavefront aberrometer for measuring refractive errors and a keratometer for measuring corneal curvature into a compact chassis. Throughout this doctoral thesis, a new version of measurement technology has been incorporated to rapidly and accurately obtain objective refraction, along with new software developed to classify different corneal curvature radii, providing the user with more detailed information about the eye and corneal astigmatism. This new device, QuickSee Free Pro Keratometry, has the potential to address the massive problem of access to corrected vision by simplifying and expediting the glasses prescription process, potentially revolutionizing the way refraction is performed globally.

Refractive errors are very common eye disorders in which the eye cannot clearly focus on images. If they are not diagnosed in time, especially during childhood, they may result in severe visual impairment and even blindness. Refractive errors can be corrected simply by wearing glasses, an effective and inexpensive remedy whose cost does not exceed $5 in developing countries. Even so, refractive errors are currently one of the major global health problems. The main cause of this situation is the shortage of eye care professionals and automated equipment capable of prescribing glasses. PlenOptika proposes as a solution the QuickSee, a low cost and easy to use autorefractor. This device improves the efficiency and scope of eyecare professionals to prescribe glasses, while enabling people with minimal training in refractive techniques such as nurses, technicians or even non-healthcare personnel to carry out measurements of refractive errors quickly and reliably.

Carlos Hernandez Torres