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

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

The detection of keratoconus has been a difficult and arduous process over the years for ophthalmologists who have devised traditional approaches of diagnosis including the slit-lamp examination and observation of thinning of the corneal. The main contribution of this paper is using deep learning models namely Resnet50 and EfficientNet to not just detect whether an eye has been infected with keratoconus or not but also accurately detect the stages of infection namely mild, moderate, and advanced. The dataset used consists of corneal topographic maps and pentacam images. Individually the models achieved 97% and 94% accuracy on the dataset. We have also employed class activated maps (CAM) to observe and help visualize which areas of the images are utilized when making classifications for the different stages of keratoconus. Using deep learning models to predict the detection and severity of the infection can drastically speed up and provide accurate results at the same time

A Systematic Approach to Big Data Analysis in Cataract Patients In Telangana State, India

Big data is the new gold, especially in healthcare. Advances in collecting and processing Electronic Medical Records (EMRs), coupled with increasing computer capabilities have resulted in an increased interest in the use of big data in healthcare. Big data require collection and analysis of data at an unprecedented scale and represents a paradigm shift in healthcare, offering on one hand the capacity to generate new knowledge more quickly than traditional scientific approaches, and, on the other hand, a holistic understanding of specific illnesses when socio-demographics are incorporated in the analysis. Big data promises more personalized and precision medicine for patients with improved accuracy and earlier diagnosis, and therapy geared to an individual’s unique combination of genes, environmental risk, and precise disease phenotype. Ophthalmology has been an area of focus where results have shown to be promising. The objective of this study was to determine whether the EMR record in LV Prasad Eye Institute (LVPEI), based in Hyderabad, India, can contribute to the management of patient care, through studying how climatic and socio-demographic factors relate to cataracts, clouding of the lens – turning the lens from clear to yellow, brown or even milky white, which cause visual impairment and blindness if left untreated. The study was designed by merging a dataset obtained from the Telangana State Development Society to an existing EMR of approximately 1 million patients, who presented themselves with different eye symptoms and were diagnosed with several ocular diseases from the years (2011-2019), a timeframe of 8 years. The dataset obtained included climatic variables to be tested alongside the development of cataracts in patients. Microsoft Power BI was used to analyze the data through prescriptive and descriptive data analysis techniques to read patterns that can dig deeper into high-risk climatic and socio-demographic factors that correlate to the development of cataract. Our findings revealed that there is a high presence of cataract in the state of Telangana, mostly in rural areas and throughout the different weather seasons in India. Women tend to be the most affected as per the number of visits to the clinic, while home makers make the most visit to the hospital, in addition to employees, students, and laborers. While cataract is most dominant in the older age population, diseases such as astigmatism and conjunctivitis, are more present in the younger age population. The study appeared useful for taking preventive measures in the future to manage the treatment of patients who present themselves with cataracts in Telangana. In addition, this research created a pathway for new methods in the study of how EMRs contribute to new knowledge in ophthalmology. Results indicated that cultural upbringing, climatic factors, and proximity to the state-run thermal plant play a significant role in the presence of cataracts. Through testing the methodology used, observations indicate that the AI technique used is only effective when variables are minimized. Reflections suggest that studying patients through a more holistic and systematic approach can reveal new insights that can help bridge the gap between existing knowledge and practice for an aim to provide enhanced ophthalmic care in India

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Articles indexats publicats per investigadors del Campus de Terrassa: 2015

Aquest informe recull els 284 treballs publicats per 218 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2015Postprint (published version

Applications of Artificial Intelligence in Medicine Practice

This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted

Clamp-assisted retractor advancement for lower eyelid involutional entropion

Scientific Poster 144PURPOSE: To describe a novel approach to internal repair of lower lid entropion using the Putterman clamp. METHODS: Retrospective, consecutive case series of patients with entropion who underwent retractor advancement using the clamp. RESULTS: Seven eyes of 6 patients (average age: 80; 4 women and 2 men) were analyzed. Complete resolution was achieved in 5 of the 6 patients (83.3%). The 1 patient with recurrence had 2 previous entropion surgeries on each eye over the past 4 years; there was lid laxity, and horizontal tightening was needed. No severe adverse events occurred in the patients. CONCLUSION: Clamp-assisted lower lid retractor advancement offers a safe and effective, minimally invasive approach to involutional entropion. Further study is needed to assess its role in recurrent entropion.postprin

NOVEL METHODS OF MERIDIONAL AND CIRCUMFERENTIAL ANTERIOR CHAMBER ANGLE IMAGING

Ph.DDOCTOR OF PHILOSOPH

Congenital Diaphragmatic Hernia

Congenital Diaphragmatic hernia (CDH) occurs in approximately 1 in every 2,500 births and the cause is yet unknown. In CDH the diaphragm fails to form correctly, allowing herniation of the abdominal contents into the thoracic cavity and results in pulmonary hypolplasia. This book describes the embryology, genetics, antenatal diagnosis, management, associated congenital anomalies and long-term outcomes of children with CDH. It is a valuable up-to-date reference for pediatricians, neonatologists and allied health professionals who care for children with CDH