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1. Ocular blood flow as a clinical observation: Value, limitations and data analysis

   Harris, A. ( January 2020 , Progress in retinal and eye research)

   Alterations in ocular blood flow have been identified as important risk factors for the onset and progression of numerous diseases of the eye. In particular, several population-based and longitudinal-based studies have provided compelling evidence of hemodynamic biomarkers as independent risk factors for ocular disease throughout several different geographic regions. Despite this evidence, the relative contribution of blood flow to ocular physiology and pathology in synergy with other risk factors and comorbidities (e.g., age, gender, race, diabetes and hypertension) remains uncertain. There is currently no gold standard for assessing all relevant vascular beds in the eye, and the heterogeneous vascular biomarkers derived from multiple ocular imaging technologies are non-interchangeable and difficult to interpret as a whole. As a result of these disease complexities and imaging limitations, standard statistical methods often yield inconsistent results across studies and are unable to quantify or explain a patient's overall risk for ocular disease. Combining mathematical modeling with artificial intelligence holds great promise for advancing data analysis in ophthalmology and enabling individualized risk assessment from diverse, multi-input clinical and demographic biomarkers. Mechanism-driven mathematical modeling makes virtual laboratories available to investigate pathogenic mechanisms, advance diagnostic ability and improve disease management. Artificial intelligence provides a novel method formore »utilizing a vast amount of data from a wide range of patient types to diagnose and monitor ocular disease. This article reviews the state of the art and major unanswered questions related to ocular vascular anatomy and physiology, ocular imaging techniques, clinical findings in glaucoma and other eye diseases, and mechanistic modeling predictions, while laying a path for integrating clinical observations with mathematical models and artificial intelligence. Viable alternatives for integrated data analysis are proposed that aim to overcome the limitations of standard statistical approaches and enable individually tailored precision medicine in ophthalmology.« less
2. Choroidal Thickness and Primary Open-Angle Glaucoma—A Narrative Review

   https://doi.org/10.3390/jcm11051209  

   Verticchio Vercellin, Alice ; Harris, Alon ; Stoner, Ari M. ; Oddone, Francesco ; Mendoza, Kristen Ann ; Siesky, Brent ( March 2022 , Journal of Clinical Medicine)

   The choroid provides the majority of blood flow to the ocular tissues and structures that facilitate the processes of retinal metabolism responsible for vision. Specifically, the choriocapillaris provides a structural network of small blood vessels that supplies the retinal ganglion cells and deep ocular tissues. Similar to retinal nerve fiber layer thickness, choroidal thickness (CT) has been suggested to represent a quantifiable health biomarker for choroidal tissues. Glaucoma is a disease with vascular contributions in its onset and progression. Despite its importance in maintaining ocular structure and vascular functionality, clinical assessments of choroidal tissues have been historically challenged by the inaccessibility of CT biomarker targets. The development of optical coherence tomography angiography and enhanced depth imaging created a framework for assessing CT and investigating its relationship to glaucomatous optic neuropathy onset and progression. Pilot studies on CT in glaucoma are conflicting—with those both in support of, and against, its clinical utility. Complicating the data are highly customized analysis methods, small sample sizes, heterogeneous patient groups, and a lack of properly designed controlled studies with CT as a primary outcome. Herein, we review the available data on CT and critically discuss its potential relevance and limitations in glaucoma disease management.
3. A multi-scale/multi-physics model for the theoretical study of the vascular configuration of retinal capillary plexuses based on OCTA data

   https://doi.org/10.1093/imammb/dqab018  

   Chiaravalli, Greta ; Guidoboni, Giovanna ; Sacco, Riccardo ; Radell, Jake ; Harris, Alon ( November 2021 , Mathematical Medicine and Biology: A Journal of the IMA)

   Abstract The retinal tissue is highly metabolically active and is responsible for translating the visual stimuli into electrical signals to be delivered to the brain. A complex vascular structure ensures an adequate supply of blood and oxygen, which is essential for the function and survival of the retinal tissue. To date, a complete understanding of the configuration of the retinal vascular structures is still lacking. Optical coherence tomography angiography has made available a huge amount of imaging data regarding the main retinal capillary plexuses, namely the superficial capillary plexuses (SCP), intermediate capillary plexuses (ICP) and deep capillary plexuses (DCP). However, the interpretation of these data is still controversial. In particular, the question of whether the three capillary plexuses are connected in series or in parallel remains a matter of debate. In this work, we address this question by utilizing a multi-scale/multi-physics mathematical model to quantify the impact of the two hypothesized vascular configurations on retinal hemodynamics and oxygenation. The response to central retinal vein occlusion (CRVO) and intraocular pressure (IOP) elevation is also simulated depending on whether the capillary plexuses are connected in series or in parallel. The simulation results show the following: (i) in the in series configuration, themore »plexuses exhibit a differential response, with DCP and ICP experiencing larger pressure drops than SCP; and (ii) in the in parallel configuration, the blood flow redistributes uniformly in the three plexuses. The different vascular configurations show different responses also in terms of oxygen profiles: (i) in the in series configuration, the outer nuclear layer, outer plexiform layer and inner nuclear layer (INL) are those most affected by CRVO and IOP elevation; and (ii) in the in parallel configuration the INL and ganglion cell layer are those most affected. The in series results are consistent with studies on paracentral acute middle maculopathy, secondary to CRVO and with studies on IOP elevation, in which DCP and ICP and the retinal tissues surrounding them are those most affected by ischemia. These findings seem to suggest that the in series configuration better describes the physiology of the vascular retinal capillary network in health and disease.« less
4. Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases

   https://doi.org/10.1186/s11689-022-09438-w  

   Gupta, Chirag ; Chandrashekar, Pramod ; Jin, Ting ; He, Chenfeng ; Khullar, Saniya ; Chang, Qiang ; Wang, Daifeng ( May 2022 , Journal of Neurodevelopmental Disorders)

   Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC networkmore »has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

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5. Early Diagnosis and Real-Time Monitoring of Regional Lung Function Changes to Prevent Chronic Obstructive Pulmonary Disease Progression to Severe Emphysema

   https://doi.org/10.3390/jcm10245811  

   Jung, Tony ; Vij, Neeraj ( December 2021 , Journal of Clinical Medicine)

   First- and second-hand exposure to smoke or air pollutants is the primary cause of chronic obstructive pulmonary disease (COPD) pathogenesis, where genetic and age-related factors predispose the subject to the initiation and progression of obstructive lung disease. Briefly, airway inflammation, specifically bronchitis, initiates the lung disease, leading to difficulty in breathing (dyspnea) and coughing as initial symptoms, followed by air trapping and inhibition of the flow of air into the lungs due to damage to the alveoli (emphysema). In addition, mucus obstruction and impaired lung clearance mechanisms lead to recurring acute exacerbations causing progressive decline in lung function, eventually requiring lung transplant and other lifesaving interventions to prevent mortality. It is noteworthy that COPD is much more common in the population than currently diagnosed, as only 16 million adult Americans were reported to be diagnosed with COPD as of 2018, although an additional 14 million American adults were estimated to be suffering from COPD but undiagnosed by the current standard of care (SOC) diagnostic, namely the spirometry-based pulmonary function test (PFT). Thus, the main issue driving the adverse disease outcome and significant mortality for COPD is lack of timely diagnosis in the early stages of the disease. The current treatmentmore »regime for COPD emphysema is most effective when implemented early, on COPD onset, where alleviating symptoms and exacerbations with timely intervention(s) can prevent steep lung function decline(s) and disease progression to severe emphysema. Therefore, the key to efficiently combatting COPD relies on early detection. Thus, it is important to detect early regional pulmonary function and structural changes to monitor modest disease progression for implementing timely interventions and effectively eliminating emphysema progression. Currently, COPD diagnosis involves using techniques such as COPD screening questionnaires, PFT, arterial blood gas analysis, and/or lung imaging, but these modalities are limited in their capability for early diagnosis and real-time disease monitoring of regional lung function changes. Hence, promising emerging techniques, such as X-ray phase contrast, photoacoustic tomography, ultrasound computed tomography, electrical impedance tomography, the forced oscillation technique, and the impulse oscillometry system powered by robust artificial intelligence and machine learning analysis capability are emerging as novel solutions for early detection and real time monitoring of COPD progression for timely intervention. We discuss here the scope, risks, and limitations of current SOC and emerging COPD diagnostics, with perspective on novel diagnostics providing real time regional lung function monitoring, and predicting exacerbation and/or disease onset for prognosis-based timely intervention(s) to limit COPD–emphysema progression.« less