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

The Role of Neurocognitive Tests in the Assessment of Adult Attention-Deficit/Hyperactivity Disorder

Despite widespread recognition that attention-deficit/hyperactivity disorder (ADHD) is a lifelong neurodevelopmental disorder, optimal methods of diagnosis among adults remain elusive. Substantial overlap between ADHD symptoms and cognitive symptoms of other mental health conditions, such as depression and anxiety, and concerns about validity in symptom reporting have made the use of neuropsychological tests in ADHD diagnostic assessment appealing. However, past work exploring the potential diagnostic utility of neuropsychological tests among adults has often relied on a relatively small subset of tests, has failed to include symptom and performance validity measures, and often does not include comparison groups of participants with commonly comorbid disorders, such as depression. The current study examined the utility of an extensive neuropsychological measure battery for diagnosing ADHD among adults. Two hundred forty-six participants (109 ADHD, 52 depressed, 85 nondisordered controls) completed a multistage screening and assessment process, which included a clinical interview, self, and informant report on behavior rating scales, performance and symptom validity measures, and an extensive neuropsychological testing battery. Results indicated that measures of working memory, sustained attention, response speed, and variability best discriminated ADHD and non-ADHD participants. While single test measures provided performed poorly in identifying ADHD participants, analyses revealed that a combined approach using self and informant symptom ratings, positive family history of ADHD, and a reaction time (RT) variability measure correctly classified 87% of cases. Findings suggest that neuropsychological test measures used in conjunction with other clinical assessments may enhance prediction of adult ADHD diagnoses

Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems