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General General

A self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS).

In BMC neurology

BACKGROUND : Cognitive impairment is common in patients with multiple sclerosis (MS). Accurate and repeatable measures of cognition have the potential to be used as markers of disease activity.

METHODS : We developed a 5-min computerized test to measure cognitive dysfunction in patients with MS. The proposed test - named the Integrated Cognitive Assessment (ICA) - is self-administered and language-independent. Ninety-one MS patients and 83 healthy controls (HC) took part in Substudy 1, in which each participant took the ICA test and the Brief International Cognitive Assessment for MS (BICAMS). We assessed ICA's test-retest reliability, its correlation with BICAMS, its sensitivity to discriminate patients with MS from the HC group, and its accuracy in detecting cognitive dysfunction. In Substudy 2, we recruited 48 MS patients, 38 of which had received an 8-week physical and cognitive rehabilitation programme and 10 MS patients who did not. We examined the association between the level of serum neurofilament light (NfL) in these patients and their ICA scores and Symbol Digit Modalities Test (SDMT) scores pre- and post-rehabilitation.

RESULTS : The ICA demonstrated excellent test-retest reliability (r = 0.94), with no learning bias, and showed a high level of convergent validity with BICAMS. The ICA was sensitive in discriminating the MS patients from the HC group, and demonstrated high accuracy (AUC = 95%) in discriminating cognitively normal from cognitively impaired participants. Additionally, we found a strong association (r = - 0.79) between ICA score and the level of NfL in MS patients before and after rehabilitation.

CONCLUSIONS : The ICA has the potential to be used as a digital marker of cognitive impairment and to monitor response to therapeutic interventions. In comparison to standard cognitive tools for MS, the ICA is shorter in duration, does not show a learning bias, and is independent of language.

Khaligh-Razavi Seyed-Mahdi, Sadeghi Maryam, Khanbagi Mahdiyeh, Kalafatis Chris, Nabavi Seyed Massood

2020-May-18

Artificial intelligence (AI), BICAMS, Digital biomarkers, Integrated cognitive assessment (ICA), Language-independent, Multiple sclerosis

General General

Digital Translucence: Adapting Telemedicine Delivery Post-COVID-19.

In Telemedicine journal and e-health : the official journal of the American Telemedicine Association

In nearly 1 month, with a rapidly expanding corona virus disease 2019 (COVID-19), telemedicine has been transformed into an essential service for delivering routine clinical care. This transformation occurred as a crisis management response-driven by the need to provide care for patients with physical distancing measures in place. However, the current rapid adoption of telemedicine presents a transitional state between one that existed before the pandemic and one that could potentially be better aligned with the delivery of a personalized model of care. Using the conceptual framework of digital translucence-situating virtual encounters with more nuanced information regarding patients-we describe the role of integrated remote monitoring and virtual care tools aligned with the patient's electronic health record for adapting telemedicine delivery post-COVID-19.

Kannampallil Thomas, Ma Jun

2020-May-18

artificial intelligence, electronic health records, m-health, pandemic, telemedicine

Public Health Public Health

Machine learning on drug-specific data to predict small molecule teratogenicity.

In Reproductive toxicology (Elmsford, N.Y.)

Pregnant women are an especially vulnerable population, given the sensitivity of a developing fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on limited human data and conflicting cases of adverse outcomes due to the exclusion of pregnant populations from randomized, controlled trials. These factors increase risk for adverse drug outcomes and reduce quality of care for pregnant populations. Herein, we propose the application of artificial intelligence to systematically predict the teratogenicity of a prescriptible small molecule from information inherent to the drug. Using unsupervised and supervised machine learning, our model probes all small molecules with known structure and teratogenicity data published in research-amenable formats to identify patterns among structural, meta-structural, and in vitro bioactivity data for each drug and its teratogenicity score. With this workflow, we discovered three chemical functionalities that predispose a drug towards increased teratogenicity and two moieties with potentially protective effects. Our models predict three clinically-relevant classes of teratogenicity with AUC = 0.8 and nearly double the predictive accuracy of a blind control for the same task, suggesting successful modeling. We also present extensive barriers to translational research that restrict data-driven studies in pregnancy and therapeutically "orphan" pregnant populations. Collectively, this work represents a first-in-kind platform for the application of computing to study and predict teratogenicity.

Challa Anup P, Beam Andrew L, Shen Min, Peryea Tyler, Lavieri Robert R, Lippmann Ethan S, Aronoff David M

2020-May-16

chemical structure, drug development, drug exposure, high-throughput screening, informatics, machine learning, teratogenicity, translational medicine

Ophthalmology Ophthalmology

Deep Learning for Dermatologists: Part II. Current Applications.

In Journal of the American Academy of Dermatology ; h5-index 79.0

Due to a convergence of the availability of large datasets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence (AI) has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning (DL) has been particularly impactful for medical image analysis. Deep learning applications have shown promising results in dermatology and other specialties including radiology, cardiology and ophthalmology. The modern clinician will benefit from an understanding of the basic features of deep learning in order to effectively use new applications as well as to better gauge their utility and limitations. In this second article of a two part series, we review the existing and emerging clinical applications of deep learning in dermatology and discuss future opportunities and limitations. Part 1 of this series offered an introduction to the basic concepts of deep learning to facilitate effective communication between clinicians and technical experts.

Puri Pranav, Comfere Nneka, Drage Lisa A, Shamim Huma, Bezalel Spencer A, Pittelkow Mark R, Davis Mark D P, Wang Michael, Mangold Aaron R, Tollefson Megha M, Lehman Julia S, Meves Alexander, Yiannias James A, Otley Clark C, Carter Rickey E, Sokumbi Olayemi, Hall Matthew R, Bridges Alina G, Murphree Dennis H

2020-May-16

artificial intelligence, deep learning, dermatology, machine learning

General General

Active Deep Learning to Detect Demographic Traits in Free-Form Clinical Notes.

In Journal of biomedical informatics ; h5-index 55.0

The free-form portions of clinical notes are a significant source of information for research, but before they can be used, they must be de-identified to protect patients' privacy. De-identification efforts have focused on known identifier types (names, ages, dates, addresses, ID's, etc.). However, a note can contain residual "Demographic Traits" (DTs), unique enough to re-identify the patient when combined with other such facts. Here we examine whether any residual risks remain after removing these identifiers. After manually annotating over 140,000 words worth of medical notes, we found no remaining directly identifying information, and a low prevalence of demographic traits, such as marital status or housing type. We developed an annotation guide to the discovered Demographic Traits (DTs) and used it to label MIMIC-III and i2b2-2006 clinical notes as test sets. We then designed a "bootstrapped" active learning iterative process for identifying DTs: we tentatively labeled as positive all sentences in the DT-rich note sections, used these to train a binary classifier, manually corrected acute errors, and retrained the classifier. This train-and-correct process may be iterated. Our active learning process significantly improved the classifier's accuracy. Moreover, our BERT-based model outperformed non-neural models when trained on both tentatively labeled data and manually relabeled examples. To facilitate future research and benchmarking, we also produced and made publicly available our human annotated DT-tagged datasets. We conclude that directly identifying information is virtually non-existent in the multiple medical note types we investigated. Demographic traits are present in medical notes, but can be detected with high accuracy using a cost-effective human-in-the-loop active learning process, and redacted if desired.2.

Feder Amir, Vainstein Danny, Rosenfeld Roni, Hartman Tzvika, Hassidim Avinatan, Matias Yossi

2020-May-16

Active Machine Learning, Data Anonymization, Deep Learning, Natural Language Processing, Personally Identifiable Information

Radiology Radiology

Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB).

In Magnetic resonance imaging

OBJECTIVE : Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI.

METHODS : We developed a deep residual network with densely connected multi-resolution blocks (DRN-DCMB) model to reduce the motion artifacts in T1 weighted (T1W) spin echo images acquired on different imaging planes before and after contrast injection. The DRN-DCMB network consisted of multiple multi-resolution blocks connected with dense connections in a feedforward manner. A single residual unit was used to connect the input and output of the entire network with one shortcut connection to predict a residual image (i.e. artifact image). The model was trained with five motion-free T1W image stacks (pre-contrast axial and sagittal, and post-contrast axial, coronal, and sagittal images) with simulated motion artifacts.

RESULTS : In other 86 testing image stacks with simulated artifacts, our DRN-DCMB model outperformed other state-of-the-art deep learning models with significantly higher structural similarity index (SSIM) and improvement in signal-to-noise ratio (ISNR). The DRN-DCMB model was also applied to 121 testing image stacks appeared with various degrees of real motion artifacts. The acquired images and processed images by the DRN-DCMB model were randomly mixed, and image quality was blindly evaluated by a neuroradiologist. The DRN-DCMB model significantly improved the overall image quality, reduced the severity of the motion artifacts, and improved the image sharpness, while kept the image contrast.

CONCLUSION : Our DRN-DCMB model provided an effective method for reducing motion artifacts and improving the overall clinical image quality of brain MRI.

Liu Junchi, Kocak Mehmet, Supanich Mark, Deng Jie

2020-May-16

Deep leaning, Dense connection, MRI, Motion artifact, Multi-resolution block, Residual learning