A Review of the Latest Machine Learning Advances in Cataract Diagnosis

Cataract disorder is one of the most common vision disorders in the world. As the average age of the world population increases, many people suffer from it in middle and old age. Timely diagnosis can prevent the reduction of vision and eventually loss of sight. Considering the prevalence of Artificial Intelligence algorithms, especially in the medical industry, they could be used for Cataract diagnosis, IOL determination, and PCO diagnosis. According to the studies, the proposed models for Cataract diagnosis are very accurate. These developed algorithms have been able to make access to ophthalmology services easier and reduce treatment costs significantly

Studies on machine learning-based aid for residency training and time difficulty in ophthalmology

兵庫県立大学大学院工学(博士)2023doctoral thesi

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 144

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1975

Development of a Multi-Site Phase II Clinical Trial of Valproic Acid for Retinitis Pigmentosa

The body of work presented here is a compendium of the multiple steps required for an investigator initiated trial of an existing medication (Valproic Acid- VPA) for a new indication (Retinitis Pigmentosa – RP). The chapters are listed in logical and chronological order of the process. In order to access patient records an expedited Institutional Review Board (IRB) application for retrospective chart review was submitted (Chapter 1). These records enabled the statistical analysis which not only laid the framework for the trial design, but also became the basis for two manuscripts (Chapter 2). Protocol development informed by the preliminary human studies (Chapter 3) was an instrumental part of the Investigational New Drug (IND) application (Chapter 3.5). This protocol along with the extensive case report forms that detail the intended data to be collected are included in the IND application. Because the Phase II clinical trial proposed attempting to identify the specific RP mutations of the subjects utilizing a National Eye Institute (NEI) study that enabled free genotyping services, two IRB applications were submitted (Chapter 3.6). The first was for approval of the NEI genotyping protocol, the second involved the VPA intervention. Two very different sources of funding for this trial were attempted (Chapter 4) – the NIH via the Challenge Grant mechanism and a private eye disease foundation (Foundation Fighting Blindness). In Chapter 5 I detail the alternate study designs that were considered and developed for this trial (and ultimately abandoned). Finally, in Chapter 6, I formally detail my suggestions to aid in the development of a comprehensive investigator initiated core facility at UMMMC. The goal of this project was two-fold. The first was to learn the entire process of trial and protocol design both from a Umass Institutional perspective as well as from the perspective of the FDA. The second goal was the very real prospect of helping patients with a blinding disease. This work was successful on both counts. IRB approval was received for all the submitted applications. The complexity and uniqueness of many aspects of these submissions culminated in a comprehensive learning experience. The process of working with the Umass Research Pharmacy as well as developing the industry contacts and know-how to develop a workable and financially feasible placebo were both particularly important learning experiences. FDA approval of the IND submission was also received, and the process of pre-communication and delving into the considerable and ever-changing rules and regulations resulted in an extensive and valuable knowledge base. While the practicality of funding has limited the ability of this trial to move forward at this point, given the extensive framework laid by this body of work, we are actively pursuing other opportunities. The third outcome of this work, while not as intentional, was the considerable process of determining the specific competencies and infrastructure that exist at UMMMC to enable investigator initiated drug intervention studies. While this institution is clearly moving rapidly in the direction of translational research, the many needs of these studies are often only clearly understood when the process is specifically undertaken. In completing the approval of this Phase II clinical trial, I was not only able to better understand and define the existing capabilities of UMMMC for this kind of research, I was able to add to that infrastructure when the existing knowledge or skill set was not available. In this manner, I was able to inform and guide many of the support personnel who guided me and have become a part of the strategic direction of UMMMC towards clinical translational research

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research

With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization

A dark field illumination probe linked to Raman spectroscopy for non-invasivety determination of ocular biomarkers

For early and effective diagnosis of eye diseases, acquiring biochemical information in the eye is preferred. However, it is obtained by performing a biopsy of the eye tissue. This poses a risk to the integrity of the eye and cannot be performed on a regular basis. Raman spectrometry is a potential and powerful tool for the non-invasive investigation of biochemical information. The challenge to use it in an ophthalmic application is the essential of a high-power laser direct shining through the eye, which raises safety concerns for potential retinal damage .In this thesis, biomedical applications of Raman spectroscopy are explored for eye disease biomarkers and ocular drug measurements in ex vitro, in vitro and in vivo. To ensure a safety measurement by projecting a laser in the eye, two types of dark-field illumination probes are designed, manufactured and validated in conjunction with confocal Raman spectroscopy (CRS) to avoid light damage of the retina. Furthermore, a non-contact dark-field illumination method for the same purpose is proposed and theoretically validated

Ex-vivo and In-vivo Characterization of Human Accommodation

A completely satisfying approach to restoring accommodation still needs to be developed. Besides, there are considerable discrepancies between objective and subjective trials to evaluate the therapeutic success. A substantial biomechanical understanding of all structures and processes involved in accommodation as well as presbyopia are needed to develop promising new strategies. This contribution focuses on developing advanced imaging techniques to create a basic understanding of accommodation and presbyopia and to evaluate existing concepts for restoring accommodation. Besides, the emphasis is also on replacing stiff presbyopic lenses by a material that imitates the young crystalline lens

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