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General General

AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates

ArXiv Preprint

The COVID-19 pandemic has profound global consequences on health, economic, social, political, and almost every major aspect of human life. Therefore, it is of great importance to model COVID-19 and other pandemics in terms of the broader social contexts in which they take place. We present the architecture of AICov, which provides an integrative deep learning framework for COVID-19 forecasting with population covariates, some of which may serve as putative risk factors. We have integrated multiple different strategies into AICov, including the ability to use deep learning strategies based on LSTM and even modeling. To demonstrate our approach, we have conducted a pilot that integrates population covariates from multiple sources. Thus, AICov not only includes data on COVID-19 cases and deaths but, more importantly, the population's socioeconomic, health and behavioral risk factors at a local level. The compiled data are fed into AICov, and thus we obtain improved prediction by integration of the data to our model as compared to one that only uses case and death data.

Geoffrey C. Fox, Gregor von Laszewski, Fugang Wang, Saumyadipta Pyne

2020-10-08

Surgery Surgery

Using conditional generative adversarial networks to reduce the effects of latency in robotic telesurgery.

In Journal of robotic surgery

The introduction of surgical robots brought about advancements in surgical procedures. The applications of remote telesurgery range from building medical clinics in underprivileged areas, to placing robots abroad in military hot-spots where accessibility and diversity of medical experience may be limited. Poor wireless connectivity may result in a prolonged delay, referred to as latency, between a surgeon's input and action which a robot takes. In surgery, any micro-delay can injure a patient severely and, in some cases, result in fatality. One way to increase safety is to mitigate the effects of latency using deep learning aided computer vision. While the current surgical robots use calibrated sensors to measure the position of the arms and tools, in this work, we present a purely optical approach that provides a measurement of the tool position in relation to the patient's tissues. This research aimed to produce a neural network that allowed a robot to detect its own mechanical manipulator arms. A conditional generative adversarial network (cGAN) was trained on 1107 frames of a mock gastrointestinal robotic surgery from the 2015 EndoVis Instrument Challenge and corresponding hand-drawn labels for each frame. When run on new testing data, the network generated near-perfect labels of the input images which were visually consistent with the hand-drawn labels and was able to do this in 299 ms. These accurately generated labels can then be used as simplified identifiers for the robot to track its own controlled tools. These results show potential for conditional GANs as a reaction mechanism, such that the robot can detect when its arms move outside the operating area in a patient. This system allows for more accurate monitoring of the position of surgical instruments in relation to the patient's tissue, increasing safety measures that are integral to successful telesurgery systems.

Sachdeva Neil, Klopukh Misha, Clair Rachel St, Hahn William Edward

2020-Oct-07

Conditional generative adversarial networks, Image segmentation, Medical image, Remote surgery, Robotic surgery

Radiology Radiology

Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept.

In European radiology ; h5-index 62.0

OBJECTIVE : To retrospectively evaluate if texture-based radiomics features are able to detect interstitial lung disease (ILD) and to distinguish between the different disease stages in patients with systemic sclerosis (SSc) in comparison with mere visual analysis of high-resolution computed tomography (HRCT).

METHODS : Sixty patients (46 females, median age 56 years) with SSc who underwent HRCT of the thorax were retrospectively analyzed. Visual analysis was performed by two radiologists for the presence of ILD features. Gender, age, and pulmonary function (GAP) stage was calculated from clinical data (gender, age, pulmonary function test). Data augmentation was performed and the balanced dataset was split into a training (70%) and a testing dataset (30%). For selecting variables that allow classification of the GAP stage, single and multiple logistic regression models were fitted and compared by using the Akaike information criterion (AIC). Diagnostic accuracy was evaluated from the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, and diagnostic sensitivity and specificity were calculated.

RESULTS : Values for some radiomics features were significantly lower (p < 0.05) and those of other radiomics features were significantly higher (p = 0.001) in patients with GAP2 compared with those in patients with GAP1. The combination of two specific radiomics features in a multivariable model resulted in the lowest AIC of 10.73 with an AUC of 0.96, 84% sensitivity, and 99% specificity. Visual assessment of fibrosis was inferior in predicting individual GAP stages (AUC 0.86; 83% sensitivity; 74% specificity).

CONCLUSION : The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features indicating severity of SSc-ILD on HRCT, which are not recognized by visual analysis.

KEY POINTS : • Radiomics features can predict GAP stage with a sensitivity of 84% and a specificity of almost 100%. • Extent of fibrosis on HRCT and a combined model of different visual HRCT-ILD features perform worse in predicting GAP stage. • The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features on HRCT, which are not recognized by visual analysis.

Martini K, Baessler B, Bogowicz M, Blüthgen C, Mannil M, Tanadini-Lang S, Schniering J, Maurer B, Frauenfelder T

2020-Oct-06

Artificial intelligence, Pulmonary fibrosis, Systemic sclerosis

oncology Oncology

Radiomic Analysis of CT Predicts Tumor Response in Human Lung Cancer with Radiotherapy.

In Journal of digital imaging

PURPOSE : Radiomics features can be positioned to monitor changes throughout treatment. In this study, we evaluated machine learning for predicting tumor response by analyzing CT images of lung cancer patients treated with radiotherapy.

EXPERIMENTAL DESIGN : For this retrospective study, screening or standard diagnostic CT images were collected for 100 patients (mean age, 67 years; range, 55-82 years; 64 men [mean age, 68 years; range, 55-82 years] and 36 women [mean age, 65 years; range, 60-72 years]) from two institutions between 2013 and 2017. Radiomics analysis was available for each patient. Features were pruned to train machine learning classifiers with 50 patients, then trained in the test dataset.

RESULT : A support vector machine classifier with 2 radiomic features (flatness and coefficient of variation) achieved an area under the receiver operating characteristic curve (AUC) of 0.91 on the test set.

CONCLUSION : The 2 radiomic features, flatness, and coefficient of variation, from the volume of interest of lung tumor, can be the biomarkers for predicting tumor response at CT.

Yan Mengmeng, Wang Weidong

2020-Oct-06

Lung cancer, Machine learning, Radiomics, Radiotherapy

General General

Technical note: preliminary insight into a new method for age-at-death estimation from the pubic symphysis.

In International journal of legal medicine

Age-at-death estimation methods are important in forensic anthropology. However, age assessment is problematic due to inter-individual variation. The subjectivity of visual scoring systems can affect the accuracy and reliability of methods as well. One of the most studied skeletal regions for age assessment is the pubic symphysis. Few studies on Spanish pubic symphysis collections have been conducted, making further research necessary as well as the sampling of more forensic skeletal collections. This study is a preliminary development of an age-at-death estimation method from the pubic symphysis based on a new simple scoring system. A documented late twentieth century skeletal collection (N = 29) and a twenty-first century forensic collection (N = 76) are used. Sixteen traits are evaluated, and a new trait (microgrooves) is described and evaluated for the first time in this study. All traits are scored in a binary manner (present or absent), thus reducing ambiguity and subjectivity. Several data sets are constructed based on different age intervals. Machine learning methods are employed to evaluate the scoring system's performance. The results show that microgrooves, macroporosity, beveling, lower extremity, ventral and dorsal margin decomposition, and lipping are the best preforming traits. The new microgroove trait proves to be a good age predictor. Reliable classification results are obtained for three age intervals (≤ 29, 30-69, ≥ 70). Older individuals are reliably classified with two age intervals (< 80, ≥ 80). The combination of binary attributes and machine learning algorithms is a promising tool for gaining objectivity in age-at-death assessment.

Castillo Andrés, Galtés Ignasi, Crespo Santiago, Jordana Xavier

2020-Oct-06

Age-at-death estimation, Age-related traits, Forensic collection, Machine learning, Pubic symphysis, Scoring system

Radiology Radiology

Improvement of the diagnostic accuracy for intracranial haemorrhage using deep learning-based computer-assisted detection.

In Neuroradiology

PURPOSE : To elucidate the effect of deep learning-based computer-assisted detection (CAD) on the performance of different-level physicians in detecting intracranial haemorrhage using CT.

METHODS : A total of 40 head CT datasets (normal, 16; haemorrhagic, 24) were evaluated by 15 physicians (5 board-certificated radiologists, 5 radiology residents, and 5 medical interns). The physicians attended 2 reading sessions without and with CAD. All physicians annotated the haemorrhagic regions with a degree of confidence, and the reading time was recorded in each case. Our CAD system was developed using 433 patients' head CT images (normal, 203; haemorrhagic, 230), and haemorrhage rates were displayed as corresponding probability heat maps using U-Net and a machine learning-based false-positive removal method. Sensitivity, specificity, accuracy, and figure of merit (FOM) were calculated based on the annotations and confidence levels.

RESULTS : In patient-based evaluation, the mean accuracy of all physicians significantly increased from 83.7 to 89.7% (p < 0.001) after using CAD. Additionally, accuracies of board-certificated radiologists, radiology residents, and interns were 92.5, 82.5, and 76.0% without CAD and 97.5, 90.5, and 81.0% with CAD, respectively. The mean FOM of all physicians increased from 0.78 to 0.82 (p = 0.004) after using CAD. The reading time was significantly lower when CAD (43 s) was used than when it was not (68 s, p < 0.001) for all physicians.

CONCLUSION : The CAD system developed using deep learning significantly improved the diagnostic performance and reduced the reading time among all physicians in detecting intracranial haemorrhage.

Watanabe Yoshiyuki, Tanaka Takahiro, Nishida Atsushi, Takahashi Hiroto, Fujiwara Masahiro, Fujiwara Takuya, Arisawa Atsuko, Yano Hiroki, Tomiyama Noriyuki, Nakamura Hajime, Todo Kenichi, Yoshiya Kazuhisa

2020-Oct-06

Computed tomography, Deep learning, Diagnosis, Efficacy, Intracranial haemorrhage, Retrospective