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

Fully automated computer system for diagnosis of corneal diseases. Development of image processing technologies for the diagnosis of Acanthamoeba and Fusarium diseases in confocal microscopy images

Confocal microscopy demonstrated its value in the diagnosis of Acanthamoeba and fungal keratitis which considered sight-threatening corneal diseases. However, it can be difficult to find and train confocal microscopy graders to accurately detect Acanthamoeba cysts and fungal filaments in the images. Use of an automated system could overcome this problem and help to start the correct treatment more quickly. Also, response to treatment can be difficult to assess in infectious keratitis using clinical examination alone, but there is evidence that the morphology of filaments and cysts may change over time with the use of correct treatment. An automated system to analyse confocal microscopy images for such changes would also assist clinicians in determining whether the ulcer is improving, or whether a change of treatment is needed. This research proposes a fully automated novel system with GUI to detect cysts and hyphae (filaments) and measure useful quantitative parameters for them through many stages; Image enhancement, image segmentation, quantitative analysis for detected cysts and hyphae, and registration and tracking of ordered sequence of images. The performance of the proposed segmentation procedure is evaluated by comparing between the manual and the automated traced images of the dataset that was provided by the Manchester Royal Eye Hospital. The positive predictive values rate of cysts for Acanthamoeba images was 76%. For detected hyphae in Fusarium images, many standard measurements were computed. The accuracy of their values was quantified by calculating the percent error rate for each measurement and which ranged from 23% to 49%

Artificial Intelligence and Medicine

The introduction of artificial intelligence (AI) has resulted in numerous technological advancements in the medical profession and a radical transformation of the old medical model. Artificial intelligence in medicine consists mostly of machine learning, deep learning, expert systems, intelligent robotics, the internet of medical things, and other prevalent and new AI technology. The primary applications of AI in the medical industry are intelligent screening, intelligent diagnosis, risk prediction, and supplemental treatment. Presently, medical AI has achieved significant advances, and big data quality management, new technology empowerment innovation, multi-domain knowledge integration, and personalized medical decision-making will exhibit greater growth potential in the clinical arena

Diagnostic Armamentarium of Infectious Keratitis: A Comprehensive Review

Infectious keratitis (IK) represents the leading cause of corneal blindness worldwide, particularly in developing countries. A good outcome of IK is contingent upon timely and accurate diagnosis followed by appropriate interventions. Currently, IK is primarily diagnosed on clinical grounds supplemented by microbiological investigations such as microscopic examination with stains, and culture and sensitivity testing. Although this is the most widely accepted practice adopted in most regions, such an approach is challenged by several factors, including indistinguishable clinical features shared among different causative organisms, polymicrobial infection, long diagnostic turnaround time, and variably low culture positivity rate. In this review, we aim to provide a comprehensive overview of the current diagnostic armamentarium of IK, encompassing conventional microbiological investigations, molecular diagnostics (including polymerase chain reaction and mass spectrometry), and imaging modalities (including anterior segment optical coherence tomography and in vivo confocal microscopy). We also highlight the potential roles of emerging technologies such as next-generation sequencing, artificial intelligence-assisted platforms. and tele-medicine in shaping the future diagnostic landscape of IK

Studies in the Diagnosis and Pathophysiology of Severe Microbial Keratitis

Background: Microbial keratitis (MK) is a major cause of blindness worldwide. Few studies have assessed the HRT3 in vivo confocal microscope (IVCM) in the diagnosis or management of MK. This PhD aimed to investigate these questions and was based in a high incidence setting for MK: Aravind Eye Hospital, Madurai, India. Methods: A prospective observational study of 252 severe MK patients was conducted with follow-up at days 7, 14 and 21 post-enrolment. Quantitative PCR of corneal swabs obtained at presentation validated transcriptome results. IVCM images were evaluated in this cohort for diagnostic accuracy, and ability to monitor outcome. Results: For fungal detection, HRT3 IVCM had a high sensitivity (85.7%; 95% CI: 82.2%– 88.6%) and specificity (81.4%; 95% CI: 76.0%–85.9%). For Acanthamoeba, the sensitivity was 88.2% (95% CI: 76.2%–94.6%) and specificity was 98.2% (95% CI: 94.9%–99.3%). Mean fungal branching angle in IVCM images was not significantly different for Fusarium sp. (59.7°; 95% CI: 57.7°–61.8°) versus Aspergillus sp. (63.3°; 95% CI: 60.8°–65.8°; p=0.07). At presentation, anterior corneal IVCM morphology associated with BK included bullae (OR 9.99, 95% CI: 3.11–32.06, p<0.001), and in FK a honeycomb distribution of inflammatory cells (OR 2.74, 95%CI: 1.01–7.40, p=0.047). Poor outcomes in FK were associated with stellate interconnected cellular processes with no visible nuclei (OR 2.28, 95% CI: 1.03-5.06, p=0.043) in baseline IVCM images, and fungal filaments (OR 6.48, 95% CI:2.50-16.78, p<0.001) or inflammatory cells in a honeycomb distribution (OR 5.24, 95% CI: 1.44-19.06, p=0.012) in final visit images. Conclusions: HRT3 IVCM can yield a high diagnostic accuracy. Fungal branching angle in IVCM images does not differentiate between Fusarium and Aspergillus keratitis. IVCM image morphologies may be associated with causative organism or clinical outcome in MK

Feline sino-nasal and sino-orbital aspergillosis

Feline sino-nasal and sino-orbital aspergillosis. This thesis characterizes upper respiratory tract aspergillosis (URTA) in domestic cats, an emerging mycosis caused by fungi from Genus Aspergillus. Affected cats were 1.5 to13 years old (median 5 y). Brachycephalic purebred cats were over-represented. Sino-orbital aspergillosis (SOA) was more common than sino-nasal aspergillosis (SNA). Fungal pathogens were cultured readily. Aspergillus fumigatus was the most common cause of SNA. In all cases of SOA, the fungal pathogen was an uncharacterised species within the Aspergillus viridinutans complex in Aspergillus section Fumigati. This pathogen, also isolated from a dog and a human with aspergillosis, was subsequently identified as a novel species, A. felis (neosartorya-morph), using a polyphasic taxonomic approach including comparative sequence analysis of the ITS, partial β-tubulin and calmodulin genes. A. felis is heterothallic with a functioning reproductive cycle, as confirmed by mating-type analysis, teleomorph induction and ascospore germination. It can be distinguished from A. viridinutans by growth at 45 °C and from A. fumigatus by lack of growth at 50 °C. Computed tomography was used to investigate pathogenesis of URTA. Findings support that the nasal cavity is the portal of entry for fungal spores in feline URTA and that extension to involve the orbit is via direct naso-orbital communication from bone lysis. Sera from cats with URTA, and two control groups were tested to detect Aspergillus-specific antibodies using an agar-gel double immunodiffusion (AGID) assay and an indirect IgG ELISA. The sensitivity (SE) of the AGID was 43% and specificity (SP) was 100%. At a cut-off value of 6 ELISA Units/mL the SE of the IgG ELISA was 95.2% and SP was 92 - 92.9%. Aspergillus-specific antibodies against A. fumigatus and four cryptic species (A. felis, A. thermomutatus, A. lentulus, A. udagawae) were detected. Detection of Aspergillus-specific antibodies by IgG ELISA has high SE and SP for diagnosis of feline URTA

In Vivo Confocal Microscopy expanding horizons in corneal imaging

Confocal microscopy is an emerging optical technique that allows the living human cornea to be imaged on a cellular level. As such, confocal microscopy enables morphologic and quantitative analysis of corneal resident cells in health and disease and provides an exciting bridge between in vivo diagnosis and ex vivo histological confirmation of pathologic processes

Microbiology for Allied Health Students

This open textbook is a remix of Openstax Microbiology, CC-BY 4.0, and created through an Affordable Learning Georgia Round Six Textbook Transformation Grant. The textbook has the following supplemental materials within this repository: This is a collection of instructional materials for the following open textbook and lab manual: Microbiology for Allied Health Students Lab Manual Microbiology for Allied Health Students Instructional Materials Authors\u27 Description: Microbiology for Allied Health Students is designed to cover the scope and sequence requirements for the single semester Microbiology course for non-majors and allied health students. The book presents the core concepts of microbiology with a focus on applications for careers in allied health. The pedagogical features of Microbiology for Allied Health Students make the material interesting and accessible to students while maintaining the career-application focus and scientific rigor inherent in the subject matter. The scope and sequence of Microbiology for Allied Health Students has been developed and vetted with input from numerous instructors at institutions across the U.S. It is designed to meet the needs of most microbiology courses allied health students. With these objectives in mind, the content of this textbook has been arranged in a logical progression from fundamental to more advanced concepts. The opening chapters present an overview of the discipline, with individual chapters focusing on cellular biology as well as each of the different types of microorganisms and the various means by which we can control and combat microbial growth. The focus turns to microbial pathogenicity, emphasizing how interactions between microbes and the human immune system contribute to human health and disease. The last several chapters of the text provide a survey of medical microbiology, presenting the characteristics of microbial diseases organized by body system. Accessible files with optical character recognition (OCR) and auto-tagging provided by the Center for Inclusive Design and Innovation.https://oer.galileo.usg.edu/biology-textbooks/1015/thumbnail.jp

28th Fungal Genetics Conference

Full abstracts from the 28th Fungal Genetics Conference Asilomar, March 17-22, 2015