Supervised machine learning based multi-task artificial intelligence classification of retinopathies

Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought 1) to differentiate normal from diseased ocular conditions, 2) to differentiate different ocular disease conditions from each other, and 3) to stage the severity of each ocular condition. Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI) were fully automatically extracted from the OCTA images. A stepwise backward elimination approach was employed to identify sensitive OCTA features and optimal-feature-combinations for the multi-task classification. For proof-of-concept demonstration, diabetic retinopathy (DR) and sickle cell retinopathy (SCR) were used to validate the supervised machine leaning classifier. The presented AI classification methodology is applicable and can be readily extended to other ocular diseases, holding promise to enable a mass-screening platform for clinical deployment and telemedicine.Comment: Supplemental material attached at the en

Retinal Blood Vessel Segmentation as a Tool to Detect Diabetic Retinopathy

The retina is an important part of the eye for humans. Inbesides its main function as part of the sense of sight, in the worldmedically, the retina after an image can be used to detect a numberdiseases, such as diabetic retinopathy. To detect a number of diseases,Retinal digital images taken using a digital fundus camera are used.In detecting diabetic retinopathy, digital images are neededsegmented retina. Nevertheless, automatic segmentation of digital imagesthe retina is a complex work, given the presence of artifactsas well as noise on the retinal digital image, evenly illuminated, intensitylow, low contrast, and varying lengths of retinal blood vessels.In this research, a blood vessel segmentation software system has been designed through three stagesimage processing, namely (i) preprocessing, (ii) improving image quality, (iii) andsegmentation of retinal blood vessels. With three image processing stages, the performance value is obtained, i.e. 84.62

Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature.

Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health as well as tumorigenesis. The retinal fundus is a window for human in vivo non-invasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We utilized 97,895 retinal fundus images from 54,813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated fractal dimension (FD) as a measure of vascular branching complexity, and vascular density. We associated these indices with 1,866 incident ICD-based conditions (median 10y follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular FD and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular FD and density identified 7 and 13 novel loci respectively, which were enriched for pathways linked to angiogenesis (e.g., VEGF, PDGFR, angiopoietin, and WNT signaling pathways) and inflammation (e.g., interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights on genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health records, biomarker, and genetic data to inform risk prediction and risk modification

Current roles of artificial intelligence in ophthalmology

Artificial intelligence (AI) studies are increasingly reporting successful results in the diagnosis and prognosis prediction of ophthalmological diseases as well as systemic disorders. The goal of this review is to detail how AI can be utilized in making diagnostic predictions to enhance the clinical setting. It is crucial to keep improving methods that emphasize clarity in AI models. This makes it possible to evaluate the information obtained from ocular imaging and easily incorporate it into therapeutic decision-making procedures. This will contribute to the wider acceptance and adoption of AI-based ocular imaging in healthcare settings combining advanced machine learning and deep learning techniques with new developments. Multiple studies were reviewed and evaluated, including AI-based algorithms, retinal images, fundus and optic nerve head (ONH) photographs, and extensive expert reviews. In these studies, carried out in various countries and laboratories of the world, it is seen those complex diagnoses, which can be detected systemic diseases from ophthalmological images, can be made much faster and with higher predictability, accuracy, sensitivity, and specificity, in addition to ophthalmological diseases, by comparing large numbers of images and teaching them to the computer. It is now clear that it can be taken advantage of AI to achieve diagnostic certainty. Collaboration between the fields of medicine and engineering foresees promising advances in improving the predictive accuracy and precision of future medical diagnoses achieved by training machines with this information. However, it is important to keep in mind that each new development requires new additions or updates to various social, psychological, ethical, and legal regulations

Multimodal imaging in radiation retinopathy following orbital metastasis

Background: Radiation retinopathy is a major cause of vision loss in patients receiving radiotherapy to the head and orbit. Diabetic retinopathy is one of the differential diagnosis owing to similar clinical features, including microaneurysms, cotton-wool spots, hard exudates, and macular edema. The only significant pathological difference is that radiation retinopathy spares pericytes, unlike in diabetic retinopathy. Multimodal imaging helps diagnose and predict the prognosis of radiation retinopathy, which is presented in this case report. Case Presentation: A 55-year-old woman diagnosed with stage-4 metastatic breast carcinoma presented with gradual diminution of vision in the left eye (OS) over 5 months. Vision in the right eye was lost because of orbital radiotherapy for orbital metastasis. The patient underwent multiple sessions of chemotherapy and radiotherapy. Examination of the left eye revealed a best-corrected distance visual acuity (BCDVA) of 20/30. Fundus examination of the OS revealed multiple cotton-wool spots and retinal hemorrhages. Fundus fluorescein angiography (FFA) showed diffuse macular leakage with capillary nonperfusion. Multicolor imaging (MCI) with Spectralis™ revealed black dots in the blue and green reflectance images, corresponding to capillary dilatation on FFA. Darker dots were more evident in the infrared images. BCDVA improved to 20/20 in OS after tapering the dose of oral steroids for 2 months, with improvements in hemorrhages and cotton-wool spots. Focal laser photocoagulation was recommended for the treatment of persistent macular edema. The patient declined further treatment, was lost to follow-up, and passed away 6 months later. Conclusions: This case highlights the importance of multimodal imaging for the identification and classification of radiation retinopathy. MCI using SpectralisTM has been described for the first time in radiation retinopathy and can be used to complement existing imaging modalities. Future studies involving more patients and a longer follow-up duration may provide better results for the applicability of these imaging modalities in the clinical setting

Discovering novel systemic biomarkers in photos of the external eye

External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about additional systemic medical conditions. We developed a deep learning system (DLS) that takes external eye photos as input and predicts multiple systemic parameters, such as those related to the liver (albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH); and blood count (Hgb, WBC, platelets). Development leveraged 151,237 images from 49,015 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA. Evaluation focused on 9 pre-specified systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869 patients with and without diabetes undergoing eye screening in 3 independent sites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared against baseline models incorporating available clinicodemographic variables (e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST>36, calcium=300, and WBC<4 on validation set A (a patient population similar to the development sets), where the AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7.3-13.2%. Our findings provide further evidence that external eye photos contain important biomarkers of systemic health spanning multiple organ systems. Further work is needed to investigate whether and how these biomarkers can be translated into clinical impact

Psychiatric Case Record

Bipolar Disorder-Mania: Patient was apparently normal one-month back, Then all of a sudden he developed sleep disturbances –mainly difficult in initiation of sleep. He also started abusing his family members for unwanted things. Subsequently, he started talking excessively and irritable. Sometimes he sings film songs and dances. He used to say that God Supreme exists in himself and so he has all the powers of Almighty. With that superior power he says that he can solve all the problems in this world. He also says that he has invented herbs to keep people young. For the past one week, he talks excessively without having an hour of sleep & wanders here and there & found excessively smoking. He becomes excessively spiritual and goes to near by villages for offering prayers to God. He takes only a little food everyday and he is very much keen in personal cleanliness. Paranoid Schizophrenia: She was apparently normal 8 months back, then she developed sleep disturbances in the form of difficult in falling asleep. She was found talking & smiling to herself at night & day with mirror gazing. She started saying that her neighbour & relatives are planning to kill herself by poisoning. In this context she had frequent quarrels with them and she refused to take food prepared by her mother in law. She left the home at night without informing any one and started wandering in the road side near her home. She was complaining that she hears voices as if her neighbour & relatives were talking about her among themselves She was not doing house hold activities for past 6 months and she was not taking care of her child. Her personal hygiene was very much deteriorated slowly as she used to take bath & brush, only if she was asked to do so. She started abusing & assaulting the strangers and family members. Generalised Anxiety Disorder: Six months back he was apparently normal. He is working as a system analyst in a private bank . He had once, made a mistake in his bank work for which he was given charges by his employer, followed this event he becomes very tense and afraid whenever his boss called him. He is very cautious that he should not commit any mistakes. Even though he is not doing so, he fears that he may commit some mistake in his work. At that moment he develops palpitation, giddiness, breathlessness, excessive sweating over palms and soles. Slowly these symptoms present through out the day even when he was not in his office, and he could not control his fearfulness. For the past 6 months he didn’t sleep well. His sleep is disturbed by bad dreams. Recurrent Depressive Disorder: Patient was apparently alright 2 months back. Then she developed sleep disturbances particularly early morning awakening, she use to wake up by 3.00 am and use to brood about herself and started crying. She was not doing her domestic work as before, as she felt excess tiredness and use to take frequent rests. She developed poor communication. She had lost her interest in pleasurable activities and was not interested in watching TV, and attending family gatherings. She stayed aloof most of the time & calm, quiet and withdrawn. She was expressing her helplessness and hopelessness about the future. She started to have decline in maintaining self care. 15 days back, she frequently expressed suicidal ideas and she had attempted suicide by hanging herself and was rescued by neighbours. 5 days back, she started talking in an irrelevant manner. She was smiling to self. She was assaulting her family members. She was suspicious that her neighbour had done black magic on her and also saying that people are talking about her. She reported hearing the voice of her neighbour scolding and threatening her. Organic Brain Syndrome – Dementia: Ten months back he was apparently alright. Then his relatives noticed himself frequently misplaces things inside his home. Then he started behaving aggressively. He was beating his wife without reason. He was roaming here and there, running out of home and wandering aimlessly. He was not able to come back home when he goes out. He was brought back to home by his relatives. Slowly he developed fearfulness and tremulousness while he was staying alone. He also started saying that his family members & neighbours were talking about himself, in this context he would make frequent quarrels with them. He also started hearing voices of known male voices abusing himself in third person. He sleeps for few hour only. He is passing urine and motion inside the house. He is asking about his brother and mother-in-law who were expired long back. He behaves abnormally such as pouring water in the plate while eating. And his relatives found the symptoms were worsened by evening. All these symptoms started insidiously, increased in severity through time and attained the present state. No history of loss of appetite / crying spells / suicidal tendencies / convulsions / fever / head injury

Oxygen Saturation of Retinal Vessels in All Stages of Diabetic Retinopathy and Correlation to Ultra-Wide Field Fluorescein Angiography

Purpose: The purpose of this study was to determine retinal hemoglobin oxygen saturation (SO2) in patients with diabetic retinopathy (DR) using retinal oximetry (RO) and to correlate the degree of retinal ischemia using intravenous fluorescein angiography (IVFA). Methods: This is a single-center cross-sectional cohort study. Twenty-seven controls and 60 adult patients with diabetes mellitus (16 without DR and 44 with DR) were enrolled. Patients were stratified according to DR severity. Using RO, SO2 was measured in major retinal arterioles (SaO2) and venules (SvO2). Using IVFA, the percentage of retinal ischemia in 31 patients with DR was calculated and correlated with RO. Results: Pairwise one-way analysis of variance (ANOVA) showed a significant increase in SaO2 and SvO2 in patients with proliferative DR (PDR) compared with controls (SaO2: PDR, 100 ± 7% vs. controls, 91 ± 4% [P = 0.003]; SvO2: PDR, 66 ± 11% vs. controls, 53 ± 6% [P < 0.00001]). The percentage of retinal ischemia also increased with DR severity: ANOVA showed a significant difference in retinal ischemia between all categories of nonproliferative DR vs. PDR: 2.31 ± 2% vs. 7.92 ± 9% (P = 0.017), respectively. Pearson two-tailed correlation showed significant correlation between SaO2 and ischemia (R = 0.467, P = 0.011). Conclusions: Hemoglobin oxygen saturation of retinal arterioles and venules increases with DR severity; SaO2 correlates with increasing ischemia measured by IVFA. Retinal oximetry may complement current imaging strategies to noninvasively augment the diagnosis and risk stratification of patients with diabetes