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General General

CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images.

In Chaos, solitons, and fractals

The COVID-19 pandemic is an emerging respiratory infectious disease, also known as coronavirus 2019. It appears in November 2019 in Hubei province (in China), and more specifically in the city of Wuhan, then spreads in the whole world. As the number of cases increases with unprecedented speed, many parts of the world are facing a shortage of resources and testing. Faced with this problem, physicians, scientists and engineers, including specialists in Artificial Intelligence (AI), have encouraged the development of a Deep Learning model to help healthcare professionals to detect COVID-19 from chest X-ray images and to determine the severity of the infection in a very short time, with low cost. In this paper, we propose CVDNet, a Deep Convolutional Neural Network (CNN) model to classify COVID-19 infection from normal and other pneumonia cases using chest X-ray images. The proposed architecture is based on the residual neural network and it is constructed by using two parallel levels with different kernel sizes to capture local and global features of the inputs. This model is trained on a dataset publically available containing a combination of 219 COVID-19, 1341 normal and 1345 viral pneumonia chest x-ray images. The experimental results reveal that our CVDNet. These results represent a promising classification performance on a small dataset which can be further achieve better results with more training data. Overall, our CVDNet model can be an interesting tool to help radiologists in the diagnosis and early detection of COVID-19 cases.

Ouchicha Chaimae, Ammor Ouafae, Meknassi Mohammed


COVID-19, Chest X-ray images, Classification, Convolutional neural network, Coronavirus, Deep learning

General General

Same data may bring conflict results: a caution to use the disruptive index

ArXiv Preprint

In the last two decades, scholars have designed various types of bibliographic related indicators to identify breakthrough-class academic achievements. In this study, we take a further step to look at properties of the promising disruptive index, thus deepening our understanding of this index and further facilitating its wise use in bibliometrics. Using publication records for Nobel laureates between 1900 and 2016, we calculate the DI of Nobel Prize-winning articles and its benchmark articles in each year and use the median DI to denote the central tendency in each year, and compare results between Medicine, Chemistry, and Physics. We find that conclusions based on DI depend on the length of their citation time window, and different citation time windows may cause different, even controversial, results. Also, discipline and time play a role on the length of citation window when using DI to measure the innovativeness of a scientific work. Finally, not all articles with DI equals to 1 were the breakthrough-class achievements. In other words, the DI stands up theoretically, but we should not neglect that the DI was only shaped by the number of citing articles and times the references have been cited, these data may vary from database to database.

Guoqiang Liang, Yi Jiang, Haiyan Hou


General General

Measurement of Nasalance Scores Without Touching the Philtrum for Better Comfort During Speech Assessment and Therapy: A Preliminary Study.

In The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association

OBJECTIVE : The Kay Pentax nasometer uses a separator plate that touches the philtrum of a patient to separate the nasal and oral sound energies for nasalance measurement. However, the separator plate can restrict the natural movement of the patient's upper lip and generate unpleasant pressure on the patient's philtrum. The present study was intended to measure nasalance scores without touching the philtrum for better comfort during speech assessment and therapy.

METHODS : Nasalance scores of 10 males and 10 females having no speech disorders were measured under 4 levels (0, 5, 10, and 15 mm) of the gap between the plate and the philtrum (denoted as plate-to-philtrum gap) using Nasometer II 6450 for nasal (Nasal Sentences) and oral (Zoo Passage) stimuli. Regression formulas were established to examine the relationships between nasalance score and plate-to-philtrum gap for the stimuli. To provide nasalance scores equivalent to those measured for the contact condition, compensation factors for the 5 mm plate-to-philtrum gap measurement condition were identified for the stimuli.

RESULTS : The nasalance scores were significantly different between the 4 different plate-to-philtrum gaps for the stimuli. Compensation factors for the Nasal Sentences and the Zoo Passage were identified as 1.17 and 0.71, respectively.

CONCLUSIONS : The 5 mm plate-to-philtrum gap condition after multiplying the compensation factors can provide equivalent nasalance scores to the conventional contact measurement condition which may provide better comfort in speech assessment and therapy.

Yang Xiaopeng, Pratama Gradiyan Budi, Choi Younggeun, You Heecheon, Tâm Nguyễn Phu’ò‘c Minh, Kim Gi-Wook, Jo Yun-Ju, Ko Myoung-Hwan


comfort, compensation factors, nasalance scores, speech therapy, touchless measurement

Surgery Surgery

Radiographic Indices Are Not Predictive of Clinical Outcomes Among 1735 Patients Indicated for Hip Arthroscopic Surgery: A Machine Learning Analysis.

In The American journal of sports medicine ; h5-index 90.0

BACKGROUND : The relationship between the preoperative radiographic indices for femoroacetabular impingement syndrome (FAIS) and postoperative patient-reported outcome measure (PROM) scores continues to be under investigation, with inconsistent findings reported.

PURPOSE : To apply a machine learning model to determine which preoperative radiographic indices, if any, among patients indicated for the arthroscopic correction of FAIS predict whether a patient will achieve the minimal clinically important difference (MCID) for 1- and 2-year PROM scores.

STUDY DESIGN : Cohort study; Level of evidence, 3.

METHODS : A total of 1735 consecutive patients undergoing primary hip arthroscopic surgery for FAIS were included from an institutional hip preservation registry. Patients underwent preoperative computed tomography of the hip, from which the following radiographic indices were calculated by a musculoskeletal radiologist: alpha angle, beta angle, sagittal center-edge angle, coronal center-edge angle, neck shaft angle, acetabular version angle, and femoral version angle. PROM scores were collected preoperatively, at 1 year postoperatively, and at 2 years postoperatively for the modified Harris Hip Score (mHHS), the Hip Outcome Score (HOS)-Activities of Daily Living (HOS-ADL) and -Sport Specific (HOS-SS), and the International Hip Outcome Tool (iHOT-33). Random forest models were created for each PROM at 1 and 2 years' follow-up, with each PROM's MCID used to establish clinical meaningfulness. Data inputted into the models included ethnicity, laterality, sex, age, body mass index, and radiographic indices. Comprehensive and separate models were built specifically to assess the association of the alpha angle, femoral version angle, coronal center-edge angle, McKibbin index, and hip impingement index with respect to each PROM.

RESULTS : As evidenced by poor area under the curves and P values >.05 for each model created, no combination of radiographic indices or isolated index (alpha angle, coronal center-edge angle, femoral version angle, McKibbin index, hip impingement index) was a significant predictor of a clinically meaningful improvement in scores on the mHHS, HOS-ADL, HOS-SS, or iHOT-33. The mean difference between 1- and 2-year PROM scores compared with preoperative values exceeded the respective MCIDs for the cohort.

CONCLUSION : In patients appropriately indicated for FAIS corrective surgery, clinical improvements can be achieved, regardless of preoperative radiographic indices, such as the femoral version angle, coronal center-edge angle, and alpha angle. No specific radiographic parameter or combination of indices was found to be predictive of reaching the MCID for any of the 4 studied hip-specific PROMs at either 1 or 2 years' follow-up.

Ramkumar Prem N, Karnuta Jaret M, Haeberle Heather S, Sullivan Spencer W, Nawabi Danyal H, Ranawat Anil S, Kelly Bryan T, Nwachukwu Benedict U


femoroacetabular impingement, hip arthroscopic surgery, machine learning, outcomes, radiographic indices

Ophthalmology Ophthalmology

Artificial intelligence enabled applications in kidney disease.

In Seminars in dialysis

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.

Chaudhuri Sheetal, Long Andrew, Zhang Hanjie, Monaghan Caitlin, Larkin John W, Kotanko Peter, Kalaskar Shashi, Kooman Jeroen P, van der Sande Frank M, Maddux Franklin W, Usvyat Len A


General General

Multi-domain convolutional neural network (MD-CNN) for radial reconstruction of dynamic cardiac MRI.

In Magnetic resonance in medicine ; h5-index 66.0

PURPOSE : Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath-holding difficulty or non-sinus rhythms. To reduce scan time, we propose a multi-domain convolutional neural network (MD-CNN) for fast reconstruction of highly undersampled radial cine images.

METHODS : MD-CNN is a complex-valued network that processes MR data in k-space and image domains via k-space interpolation and image-domain subnetworks for residual artifact suppression. MD-CNN exploits spatio-temporal correlations across timeframes and multi-coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective-gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD-CNN and k-t Radial Sparse-Sense(kt-RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD-CNN images were evaluated quantitatively using mean-squared-error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5-point Likert-scale (1-non-diagnostic, 2-poor, 3-fair, 4-good, and 5-excellent).

RESULTS : MD-CNN showed improved MSE and SSIM compared to kt-RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD-CCN significantly outperformed kt-RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end-diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end-systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01).

CONCLUSION : MD-CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt-RASPS.

El-Rewaidy Hossam, Fahmy Ahmed S, Pashakhanloo Farhad, Cai Xiaoying, Kucukseymen Selcuk, Csecs Ibolya, Neisius Ulf, Haji-Valizadeh Hassan, Menze Bjoern, Nezafat Reza


Cardiac MRI, deep learning, image reconstruction, non-cartesian acquisition, radial acquisition, real-time imaging