Creation and Testing of a Deep Learning Algorithm to Automatically Identify and Label Vessels, Nerves, Tendons, and Bones on Cross‐sectional Point‐of‐Care Ultrasound Scans for Peripheral Intravenous Catheter Placement by Novices

2020 ◽  
Vol 39 (9) ◽  
pp. 1721-1727
Author(s):  
Michael Blaivas ◽  
Robert Arntfield ◽  
Matthew White
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Point Of Care ◽  
Catheter Placement ◽  
Point Of Care Ultrasound ◽  
Intravenous Catheter ◽  
Cross Sectional ◽  
Deep Learning Algorithm ◽  
Peripheral Intravenous Catheter

scholarly journals Feasibility of using deep learning to detect coronary artery disease based on facial photo

2020 ◽  
Vol 41 (46) ◽  
pp. 4400-4411 ◽  
Author(s):  
Shen Lin ◽  
Zhigang Li ◽  
Bowen Fu ◽  
Sipeng Chen ◽  
Xi Li ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Test Group ◽  
Cross Sectional ◽  
Deep Learning Algorithm ◽  
Increased Risk ◽  
Artery Disease

Abstract Aims Facial features were associated with increased risk of coronary artery disease (CAD). We developed and validated a deep learning algorithm for detecting CAD based on facial photos. Methods and results We conducted a multicentre cross-sectional study of patients undergoing coronary angiography or computed tomography angiography at nine Chinese sites to train and validate a deep convolutional neural network for the detection of CAD (at least one ≥50% stenosis) from patient facial photos. Between July 2017 and March 2019, 5796 patients from eight sites were consecutively enrolled and randomly divided into training (90%, n = 5216) and validation (10%, n = 580) groups for algorithm development. Between April 2019 and July 2019, 1013 patients from nine sites were enrolled in test group for algorithm test. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using radiologist diagnosis as the reference standard. Using an operating cut point with high sensitivity, the CAD detection algorithm had sensitivity of 0.80 and specificity of 0.54 in the test group; the AUC was 0.730 (95% confidence interval, 0.699–0.761). The AUC for the algorithm was higher than that for the Diamond–Forrester model (0.730 vs. 0.623, P < 0.001) and the CAD consortium clinical score (0.730 vs. 0.652, P < 0.001). Conclusion Our results suggested that a deep learning algorithm based on facial photos can assist in CAD detection in this Chinese cohort. This technique may hold promise for pre-test CAD probability assessment in outpatient clinics or CAD screening in community. Further studies to develop a clinical available tool are warranted.

scholarly journals Pan-cancer image-based detection of clinically actionable genetic alterations

2019 ◽  
Author(s):  
Jakob Nikolas Kather ◽  
Lara R. Heij ◽  
Heike I. Grabsch ◽  
Loes F. S. Kooreman ◽  
Chiara Loeffler ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Point Of Care ◽  
Point Mutations ◽  
Turnaround Time ◽  
Genetic Alterations ◽  
Tumor Subtypes ◽  
Deep Learning Algorithm ◽  
Point Of Care Diagnostics ◽  
Personalized Cancer

Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular biology assays.1 These tests can be a bottleneck in oncology workflows because of high turnaround time, tissue usage and costs.2 Here, we show that deep learning can predict point mutations, molecular tumor subtypes and immune-related gene expression signatures3,4 directly from routine histological images of tumor tissue. We developed and systematically optimized a one-stop-shop workflow and applied it to more than 4000 patients with breast5, colon and rectal6, head and neck7, lung8,9, pancreatic10, prostate11 cancer, melanoma12 and gastric13 cancer. Together, our findings show that a single deep learning algorithm can predict clinically actionable alterations from routine histology data. Our method can be implemented on mobile hardware14, potentially enabling point-of-care diagnostics for personalized cancer treatment in individual patients.

Utilizing Deep Learning to Analyze Whole Slide Images of Colonic Biopsies for Associations Between Eosinophil Density and Clinicopathologic Features in Active Ulcerative Colitis

2021 ◽  
Author(s):  
Niels Vande Casteele ◽  
Jonathan A Leighton ◽  
Shabana F Pasha ◽  
Frank Cusimano ◽  
Aart Mookhoek ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Deep Learning ◽  
Clinical Features ◽  
Learning Algorithm ◽  
Correlation Coefficients ◽  
Disease Extent ◽  
Perfect Agreement ◽  
Cross Sectional ◽  
Deep Learning Algorithm ◽  
Colonic Biopsies

Abstract Background Eosinophils have been implicated in the pathogenesis of ulcerative colitis and have been associated with disease course and therapeutic response. However, associations between eosinophil density, histologic activity, and clinical features have not been rigorously studied. Methods A deep learning algorithm was trained to identify eosinophils in colonic biopsies and validated against pathologists’ interpretations. The algorithm was applied to sigmoid colon biopsies from a cross-sectional cohort of 88 ulcerative colitis patients with histologically active disease as measured by the Geboes score and Robarts histopathology index (RHI). Associations between eosinophil density, histologic activity, and clinical features were determined. Results The eosinophil deep learning algorithm demonstrated almost perfect agreement with manual eosinophil counts determined by 4 pathologists (interclass correlation coefficients: 0.805–0.917). Eosinophil density varied widely across patients (median 113.5 cells per mm2, interquartile range 108.9). There was no association between eosinophil density and RHI (P = 0.5). Significant differences in eosinophil density were seen between patients with Montreal E3 vs E2 disease (146.2 cells per mm2 vs 88.2 cells per mm2, P = 0.005). Patients on corticosteroids had significantly lower eosinophil density (62.9 cells per mm2 vs 124.1 cells per mm2, P = 0.006). No association between eosinophil density and biologic use was observed (P = 0.5). Conclusions We developed a deep learning algorithm to quantify eosinophils in colonic biopsies. Eosinophil density did not correlate with histologic activity but did correlate with disease extent and corticosteroid use. Future studies applying this algorithm in larger cohorts with longitudinal follow-up are needed to further elucidate the role of eosinophils in ulcerative colitis.

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