scholarly journals Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Mohammad R. Arbabshirani ◽  
Brandon K. Fornwalt ◽  
Gino J. Mongelluzzo ◽  
Jonathan D. Suever ◽  
Brandon D. Geise ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Intracranial Hemorrhage ◽  
Action Identification ◽  
Clinical Workflow ◽  
Computed Tomography Scans ◽  
Workflow Integration

Abstract WP423: Advanced Machine Learning in Action: Radiomic Based Outcome Prediction of Acute Intracranial Hemorrhage on Computed Tomography

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Jawed Nawabi ◽  
Helge Kniep ◽  
Gerhard Schön ◽  
Jens Fiehler ◽  
Uta Hanning
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Clinical Outcome ◽  
Intracranial Hemorrhage ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
University Hospital ◽  
Clinical Workflow ◽  
Workflow Integration ◽  
Risk Patients

Background: Intracranial hemorrhage (ICH) requires prompt diagnosis to optimize patient outcomes 1 . We hypothesized that machine learning algorithms could automatically analyze non-contrast computed tomography (NECT) of the head and predict clinical outcome of ICH patients 2 . Methods: 300 NECTs with acute spontaneous ICH between 2014-2019 were retrospectively included from the database at a tertiary university hospital. A binary outcome was defined as Modified Ranking Scale (mRS) 0-3 (good outcome) and mRS 4-6 (bad outcome) at discharge. Radiomic features including shape, histogram and texture markers were extracted from non- , wavelet- and log-sigma-filtered images using regions of interest of ICH. The quantitative predictors were evaluated utilizing random forest algorithms with 5-fold model-external cross-validation. Results: The model achieved an area under the ROC curve of 0.81 (95% CI [0.077; 0.86]; P<0.01), specificities and sensitivities reached 78% at Youden’s Index optimal cut-off point for the prediction of functional clinical outcome at discharge (mRS). Discussion: In conclusion, quantitative features of acute NECT images in a machine learning algorithm provided high discriminatory power in predicting functional outcome. In clinical routine, this proposed approach could allow early triage of high-risk patients for poor outcome. Indication of source:1 Qureshi, A. I. et al. Intracerebral haemorrhage. Lancet. 2009. 2 Mohammad R. Arbabshirani et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digital Medicine. 2018.

Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning

2020 ◽  
Vol 36 ◽  
pp. 101460
Author(s):  
Christian Gobert ◽  
Andelle Kudzal ◽  
Jennifer Sietins ◽  
Clara Mock ◽  
Jessica Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
X Ray ◽  
Computed Tomography Scans ◽  
X Ray Computed

scholarly journals Hybrid Convolutional Neural Network-Based Diagnosis System for Intracranial Hemorrhage

2021 ◽  
Vol 12 (4) ◽  
pp. 01-27
Author(s):  
Sezin Barin ◽  
Murat Saribaş ◽  
Beyza Gülizar Çiltaş ◽  
Gür Emre Güraksin ◽  
Utku Köse
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Intracranial Hemorrhage ◽  
Hybrid Method ◽  
Computer Aided Design ◽  
Cranial Computed Tomography ◽  
Critical Problem ◽  
Computed Tomography Scans ◽  
Aided Design

Early diagnosis of intracranial hemorrhage significantly reduces mortality. Hemorrhage is diagnosed by using various imaging methods and the most time-efficient one among them is computed tomography (CT). However, it is clear that accurate CT scans requires time, diligence, and experience. Computer-aided design methods are vital for the treatment because they facilitate early diagnosis of intracranial hemorrhage. At this point, deep learning can provide effective outcomes through an automated diagnosis way. However, as different from the known solutions, diagnosis of five different hemorrhage subtypes is a critical problem to be solved.This study focused on deep learning methods and employed cranial computed tomography scans in order to detect intracranial hemorrhage. The diagnosis approach in the study aimed to detect five subtypes of hemorrhage. In detail, EfficientNet-B3 and ResNet-Inception-V2 architectures were used for diagnosis purposes. Eventually, the study also proposed a two-architecture hybrid method for the diagnosis purpose. The obtained findings by the hybrid method were evaluated in terms of a comparative perspective.Results showed that the newly designed hybrid method was quite effective in terms of increasing classification rates of detecting intracranial hemorrhage according to the subtypes. Briefly, an accuracy of 98.5%, which is higher than those of the EfficientNet-B3 and the Inception-ResNet-V2, were obtained thanks to the developed hybrid method.

Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans

2021 ◽  
pp. 746-757
Author(s):  
Rajesh P. Shah ◽  
Heather M. Selby ◽  
Pritam Mukherjee ◽  
Shefali Verma ◽  
Peiyi Xie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Support Vector ◽  
Support Vector Classifier ◽  
Computed Tomography Scans ◽  
Group A ◽  
Group B

PURPOSE Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size. MATERIALS AND METHODS Computed tomography scans from a single institution were selected and resampled to 1 × 1 × 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance. RESULTS A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve for RF, support vector classifier, XGBoost, and logistic regression was 0.81, 0.77, 0.84, and 0.84 in group A, and 0.88, 0.87, 0.85, and 0.81 in group B, respectively. Nine radiomic features in group A and 14 radiomic features in group B were predictive of SCLC. Six radiomic features overlapped between groups A and B. CONCLUSION A machine learning radiomics model may help differentiate SCLC from other lung lesions.

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