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Ophthalmology Ophthalmology

Alterations of brain activity and functional connectivity in transition from acute to chronic tinnitus.

In Human brain mapping

The objective of this study was to investigate alterations to brain activity and functional connectivity in patients with tinnitus, exploring neural features in the transition from acute to chronic phantom perception. Twenty-four patients with acute tinnitus, 23 patients with chronic tinnitus, and 32 healthy controls were recruited. High-density electroencephalography (EEG) was used to explore changes in brain areas and functional connectivity in different groups. When compared with healthy subjects, acute tinnitus patients had a significant reduction in superior frontal cortex activity across all frequency bands, whereas chronic tinnitus patients had a significant reduction in the superior frontal cortex at beta 3 and gamma frequency bands as well as a significant increase in the inferior frontal cortex at delta-band and superior temporal cortex at alpha 1 frequency band. When compared to the chronic tinnitus group, the acute tinnitus group activity was significantly increased in the middle frontal and parietal gyrus at the gamma-band. Functional connectivity analysis showed that the chronic tinnitus group had increased connections between the parahippocampus gyrus, posterior cingulate cortex, and precuneus when compared with the healthy group. Alterations of local brain activity and connections between the parahippocampus gyrus and other nonauditory areas appeared in the transition from acute to chronic tinnitus. This indicates that the appearance and development of tinnitus is a dynamic process involving aberrant local neural activity and abnormal connectivity in multifunctional brain networks.

Lan Liping, Li Jiahong, Chen Yanhong, Chen Wan, Li Wenrui, Zhao Fei, Chen Guisheng, Liu Jiahao, Chen Yuchen, Li Yuanqing, Wang Chang-Dong, Zheng Yiqing, Cai Yuexin


acute tinnitus, chronic tinnitus, local neural activity, multifunctional brain network, transition

General General

Target adverse event profiles for predictive safety in the post-market setting.

In Clinical pharmacology and therapeutics

We improved a previous pharmacological target adverse-event profile model to predict adverse events on FDA drug labels at the time of approval. The new model uses more drugs and features for learning as well as a new algorithm. Comparator drugs sharing similar target activities to a drug of interest were evaluated by aggregating adverse events from the FDA Adverse Event Reporting System (FAERS), FDA drug labels, and medical literature. An ensemble machine learning model was used to evaluate FAERS case count, disproportionality scores, percent of comparator drug labels with a specific adverse event, and percent of comparator drugs with the reports of the event in the literature. Overall classifier performance was F1 of 0.71, area under the precision-recall curve of 0.78, and area under the receiver operating characteristic curve of 0.87. Target adverse-event analysis continues to show promise as a method to predict adverse events at the time of approval.

Schotland Peter, Racz Rebecca, Jackson David B, Soldatos Theodoros G, Levin Robert, Strauss David, Burkhart Keith


General General

EEG-based deep learning model for the automatic detection of clinical depression.

In Physical and engineering sciences in medicine

Clinical depression is a neurological disorder that can be identified by analyzing the Electroencephalography (EEG) signals. However, the major drawback in using EEG to accurately identify depression is the complexity and variation that exist in the EEG of a depressed individual. There are several strategies for automated depression diagnosis, but they all have flaws, which make the diagnostic task inaccurate. In this paper, a deep model is designed in which an integration of Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) is implemented for the detection of depression. CNN and LSTM are used to learn the local characteristics and the EEG signal sequence, respectively. In the deep learning model, filters in the convolution layer are convolved with the input signal to generate feature maps. All the extracted features are given to the LSTM for it to learn the different patterns in the signal, after which the classification is performed using fully connected layers. LSTM has memory cells to remember the essential features for a long time. It also has different functions to update the weights during training. Testing of the model was done by random splitting technique and obtained 99.07% and 98.84% accuracies for the right and left hemispheres EEG signals, respectively.

Thoduparambil Pristy Paul, Dominic Anna, Varghese Surekha Mariam



General General

Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis.

In Brain informatics

BACKGROUND : With the growing number of the aged population, the number of Parkinson's disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients' symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries.

METHOD : This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients' feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress.

RESULT : The highest accuracy in PD detection using offline data was [Formula: see text] from voice data and [Formula: see text] from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient's gender, we could improve the detection accuracy. This study's novelty is the application of ensemble averaging on the combined decisions generated from the analysis of voice and tremor data. The average accuracy of PD detection becomes [Formula: see text] when ensemble averaging was performed on majority-vote from kNN, SVM, and NB.

CONCLUSION : The proposed system can detect PD using a cloud-based system for computation, data preserving, and regular monitoring of voice and tremor samples captured by smartphones. Thus, this system can be a solution for healthcare authorities to ensure the older population's accessibility to a better medical diagnosis system in the developing countries, especially in the pandemic situation like COVID-19, when in-person monitoring is minimal.

Sajal Md Sakibur Rahman, Ehsan Md Tanvir, Vaidyanathan Ravi, Wang Shouyan, Aziz Tipu, Mamun Khondaker Abdullah Al


Accelerometer, Machine-learning, Parkinson’s, Telemonitoring, Tremor

Ophthalmology Ophthalmology

Mesopic and Scotopic Light Sensitivity and Its Microstructural Correlates in Pseudoxanthoma Elasticum.

In JAMA ophthalmology ; h5-index 58.0

Importance : Correlates for Bruch membrane alterations are needed for interventional trials targeting the Bruch membrane in pseudoxanthoma elasticum (PXE).

Objectives : To quantify mesopic and scotopic light sensitivity and identify its microstructural correlates associated with a diseased Bruch membrane in patients with PXE.

Design, Setting, and Participants : A prospective, single-center, cross-sectional case-control study was conducted at a tertiary referral center from January 31, 2018, to February 20, 2020. Twenty-two eyes of 22 patients with PXE and 40 eyes of 40 healthy individuals were included. Data analysis was completed March 15, 2020.

Exposures : Mesopic and dark-adapted 2-color fundus-controlled perimetry (microperimetry) and multimodal retinal imaging including spectral-domain optical coherence tomography (SD-OCT) and OCT angiography were performed. Perimetry thresholds were analyzed using mixed models, and structure-function correlation with SD-OCT data was performed using machine learning.

Main Outcomes and Measures : Observed dark-adapted cyan sensitivity loss as measure of rod photoreceptor dysfunction, as well as mean absolute error between predicted and observed retinal sensitivity to assess the accuracy of structure-function correlation.

Results : Of the 22 patients with PXE included in this study, 15 were women (68%); median age was 56.5 years (interquartile range, 50.4-61.2). These patients exhibited mesopic (estimate, 5.13 dB; 95% CI, 2.89-7.38 dB), dark-adapted cyan (estimate, 9.08 dB; 95% CI, 6.34-11.82 dB), and dark-adapted red (estimate, 7.05 dB; 95% CI, 4.83-9.27 dB) sensitivity losses. This sensitivity loss was also evident in 9 eyes with nonneovascular PXE (mesopic: estimate, 3.21 dB; 95% CI, 1.28-5.14 dB; dark-adapted cyan: 5.93 dB; 95% CI, 3.59-8.27 dB; and dark-adapted red testing: 4.84 dB; 95% CI, 2.88-6.80 dB), showing a distinct centrifugal pattern of sensitivity loss with preserved function toward the periphery. Retinal function could be predicted from microstructure with high accuracy (mean absolute errors, of 4.91 dB for mesopic, 5.44 dB for dark-adapted cyan, and 4.99 dB for dark-adapted red). The machine learning-based analysis highlighted an association of a thinned inner retina and putative separation of the pigment-epithelium-photoreceptor complex with sensitivity loss.

Conclusions and Relevance : In this study, among 22 patients with PXE, those with and without choroidal neovascularization exhibited reductions of retinal sensitivity being most pronounced in dark-adapted cyan testing. This finding suggests that pathologic characteristics of this Bruch membrane disease may be dominated by rod photoreceptor degeneration and/or dysfunction. A putative pigment-epithelium-photoreceptor separation may further impair rod function, while inner retinal abnormalities appear to be correlated with overall dysfunction.

Hess Kristina, Gliem Martin, Charbel Issa Peter, Birtel Johannes, Müller Philipp L, von der Emde Leon, Herrmann Philipp, Holz Frank G, Pfau Maximilian


Public Health Public Health

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption.

OBJECTIVE : This systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents.

METHODS : PubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another.

RESULTS : A total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback.

CONCLUSIONS : The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents.


Milne-Ives Madison, de Cock Caroline, Lim Ernest, Shehadeh Melissa Harper, de Pennington Nick, Mole Guy, Normando Eduardo, Meinert Edward


artificial intelligence, avatar, chatbot, conversational agent, digital health, intelligent assistant, speech recognition software, virtual assistant, virtual coach, virtual health care, virtual nursing, voice recognition software