Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images.

In Journal of biomolecular structure & dynamics

In the hospital, because of the rise in cases daily, there are a small number of COVID-19 test kits available. For this purpose, a rapid alternative diagnostic choice to prevent COVID-19 spread among individuals must be implemented as an automatic detection method. In this article, the multi-objective optimization and deep learning-based technique for identifying infected patients with coronavirus using X-rays is proposed. J48 decision tree approach classifies the deep feature of corona affected X-ray images for the efficient detection of infected patients. In this study, 11 different convolutional neural network-based (CNN) models (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet) are developed for detection of infected patients with coronavirus pneumonia using X-ray images. The efficiency of the proposed model is tested using k-fold cross-validation method. Moreover, the parameters of CNN deep learning model are tuned using multi-objective spotted hyena optimizer (MOSHO). Extensive analysis shows that the proposed model can classify the X-ray images at a good accuracy, precision, recall, specificity and F1-score rates. Extensive experimental results reveal that the proposed model outperforms competitive models in terms of well-known performance metrics. Hence, the proposed model is useful for real-time COVID-19 disease classification from X-ray chest images. Communicated by Ramaswamy H. Sarma.

Dhiman Gaurav, Chang Victor, Kant Singh Krishna, Shankar Achyut


CNN, COVID-19, Coronavirus, J48, MOSHO, deep learning, optimization

General General

Use of Artificial Intelligence to understand adults' thoughts and behaviours relating to COVID-19.

In Perspectives in public health

AIMS : The outbreak of severe acute respiratory syndrome coronavirus 2 (COVID-19) is a global pandemic that has had substantial impact across societies. An attempt to reduce infection and spread of the disease, for most nations, has led to a lockdown period, where people's movement has been restricted resulting in a consequential impact on employment, lifestyle behaviours and wellbeing. As such, this study aimed to explore adults' thoughts and behaviours in response to the outbreak and resulting lockdown measures.

METHODS : Using an online survey, 1126 adults responded to invitations to participate in the study. Participants, all aged 18 years or older, were recruited using social media, email distribution lists, website advertisement and word of mouth. Sentiment and personality features extracted from free-text responses using Artificial Intelligence methods were used to cluster participants.

RESULTS : Findings demonstrated that there was varied knowledge of the symptoms of COVID-19 and high concern about infection, severe illness and death, spread to others, the impact on the health service and on the economy. Higher concerns about infection, illness and death were reported by people identified at high risk of severe illness from COVID-19. Behavioural clusters, identified using Artificial Intelligence methods, differed significantly in sentiment and personality traits, as well as concerns about COVID-19, actions, lifestyle behaviours and wellbeing during the COVID-19 lockdown.

CONCLUSIONS : This time-sensitive study provides important insights into adults' perceptions and behaviours in response to the COVID-19 pandemic and associated lockdown. The use of Artificial Intelligence has identified that there are two behavioural clusters that can predict people's responses during the COVID-19 pandemic, which goes beyond simple demographic groupings. Considering these insights may improve the effectiveness of communication, actions to reduce the direct and indirect impact of the COVID-19 pandemic and to support community recovery.

Flint S W, Piotrkowicz A, Watts K


Artificial Intelligence, COVID-19, attitudes, behaviours, lockdown

Pathology Pathology

Anatomical and Pathological Observation and Analysis of SARS and COVID-19: Microthrombosis Is the Main Cause of Death.

In Biological procedures online

The spread of the coronavirus (SARS-CoV-2, COVID-19 for short) has caused a large number of deaths around the world. We summarized the data reported in the past few months and emphasized that the main causes of death of COVID-19 patients are DAD (Diffuse Alveolar Damage) and DIC (Disseminated intravascular coagulation). Microthrombosis is a prominent clinical feature of COVID-19, and 91.3% of dead patients had microthrombosis.Endothelial damage caused by SARS-CoV-2 cell invasion and subsequent host response disorders involving inflammation and coagulation pathways play a key role in the progression of severe COVID-19. Microvascular thrombosis may lead to microcirculation disorders and multiple organ failure lead to death.The characteristic pathological changes of DAD include alveolar epithelial and vascular endothelial injury, increased alveolar membrane permeability, large numbers of neutrophil infiltration, alveolar hyaline membrane formation, and hypoxemia and respiratory distress as the main clinical manifestations. DAD leads to ARDS in COVID-19 patients. DIC is a syndrome characterized by the activation of systemic intravascular coagulation, which leads to extensive fibrin deposition in the blood. Its occurrence and development begin with the expression of tissue factor and interact with physiological anticoagulation pathways. The down-regulation of fibrin and the impaired fibrinolysis together lead to extensive fibrin deposition.DIC is described as a decrease in the number of platelets and an increase in fibrin degradation products, such as D-dimer and low fibrinogen. The formation of microthrombus leads to the disturbance of microcirculation, which in turn leads to the death of the patient. However, the best prevention and treatment of COVID-19 microthrombosis is still uncertain.This review discusses the latest findings of basic and clinical research on COVID-19-related microthrombosis, and then we proposed the theory of microcirculation perfusion bundle therapy to explore effective methods for preventing and treating COVID-19-related microthrombosis. Further research is urgently needed to clarify how SARS-CoV-2 infection causes thrombotic complications, and how it affects the course and severity of the disease. To cultivate a more comprehensive understanding of the underlying mechanism of this disease. Raise awareness of the importance of preventing and treating microthrombosis in patients with COVID-19.

Chen Wenjing, Pan Jing Ye


Autopsy, COVID-19, Diffuse Alveolar Damage, Disseminated Intravascular Coagulation, Microcirculation Dysfunction, Pathology, SARS-CoV-2

General General

Chemistry42: An AI-based platform for de novo molecular design

ArXiv Preprint

Chemistry42 is a software platform for de novo small molecule design that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methods. Chemistry42 is unique in its ability to generate novel molecular structures with predefined properties validated through in vitro and in vivo studies. Chemistry42 is a core component of Insilico Medicine drug discovery suite that also includes target discovery and multi-omics data analysis (PandaOmics) and clinical trial outcomes predictions (InClinico).

Yan A. Ivanenkov, Alex Zhebrak, Dmitry Bezrukov, Bogdan Zagribelnyy, Vladimir Aladinskiy, Daniil Polykovskiy, Evgeny Putin, Petrina Kamya, Alexander Aliper, Alex Zhavoronkov


General General

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning.

In Journal of neural engineering ; h5-index 52.0

OBJECTIVE : Brain-machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs.

APPROACH : We propose entire spiking activity (ESA) -an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique- as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks.

MAIN RESULTS : Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data.

SIGNIFICANCE : Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.

Ahmadi Nur, Constandinou Timothy, Bouganis Christos-Savvas


brain-machine interface, deep learning, entire spiking activity, neural decoding, quasi-recurrent neural network

General General

Image-level supervised segmentation for human organs with confidence cues.

In Physics in medicine and biology

Image segmentation for human organs is an important task for diagnosis and treatment of diseases. Current deep learning-based methods are fully supervised that need pixel-level labels. Since the medical images are highly specialized and complex, the work of delineating pixel-level segmentation masks is very time-consuming. Weakly supervised methods are then chosen to lighten the workload, which only needs physicians to determine whether an image contains the organ regions of interest. While these weakly supervised methods have a common drawback. They do not incorporate prior knowledge that alleviates the lack of pixel-level information for segmentation. In this work, we propose a weakly supervised method based on prior knowledge for the segmentation of human organs. The proposed method was validated on three data sets of human organ segmentation. Experimental results show that the proposed image-level supervised segmentation method outperforms several state-of-the-art methods.

Chen Zhang, Tian Zhiqiang, Zheng Yaoyue, Si Xiangyu, Qin Xulei, Shi Zhong, Zheng Shuai


Image-level labels, Location confidence, Organ image segmentation, Size confidence, Weakly supervised learning