Quantification of damage detection schemes using receiver operating characteristic (ROC) curves

2007 ◽  
Author(s):  
Stephen Trickey ◽  
Mark Seaver ◽  
Jon Nichols
Keyword(s):  
Damage Detection ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Detection Schemes ◽  
Receiver Operating

Use of Receiver Operating Characteristic (ROC) Curves to Evaluate Computer Confidence Threshold and Clinical Performance in the Diagnosis of Appendicitis

1978 ◽  
Vol 17 (03) ◽  
pp. 157-161 ◽  
Author(s):  
F. T. De Dombal ◽  
Jane C. Horrocks
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Clinical Performance ◽  
Roc Curves ◽  
Final Diagnosis ◽  
Confidence Threshold ◽  
Computer Aided ◽  
Computer Confidence ◽  
Overall Performance ◽  
Receiver Operating

This paper uses simple receiver operating characteristic (ROC) curves (i) to study the effect of varying computer confidence of threshold levels and (ii) to evaluate clinical performance in the diagnosis of acute appendicitis. Over 1300 patients presenting to five centres with abdominal pain of short duration were studied in varying detail. Clinical and computer-aided diagnostic predictions were compared with the »final« diagnosis. From these studies it is concluded the simplistic setting of a 50/50 confidence threshold for the computer program is as »good« as any other. The proximity of a computer-aided system changed clinical behaviour patterns; a higher overall performance level was achieved and clinicians performance levels became associated with the »mildly conservative« end of the computers ROC curve. Prior forecasts of over-confidence or ultra-caution amongst clinicians using the computer-aided system have not been fulfilled.

scholarly journals MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 949
Author(s):  
Cecil J. Weale ◽  
Don M. Matshazi ◽  
Saarah F. G. Davids ◽  
Shanel Raghubeer ◽  
Rajiv T. Erasmus ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Tolerance Test ◽  
Cross Sectional Study ◽  
Oral Glucose ◽  
Cross Sectional ◽  
Receiver Operating

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies.

SOME EFFECTS OF IMAGE SEGMENTATION ON SUBSPACE-BASED AND COVARIANCE-BASED DETECTION OF ANOMALOUS SUB-PIXEL MATERIALS

2008 ◽  
Vol 18 (02) ◽  
pp. 349-367
Author(s):  
CHRISTOPHER GITTINS ◽  
DAISEI KONNO ◽  
MICHAEL HOKE ◽  
ANTHONY RATKOWSKI
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Synthetic Spectrum ◽  
Ratio Test ◽  
Probability Of False Alarm ◽  
Near Ir ◽  
Data Segmentation ◽  
Material Detection ◽  
Receiver Operating

In this paper we assess the effect that clustering pixels into spectrally-similar background types, for example, soil, vegetation, and water in hyperspectral visible/near-IR/SWIR imagery, prior to applying a detection methodology has on material detection statistics. Specifically, we examine the effects of data segmentation on two statistically-based detection metrics, the Subspace Generalized Likelihood Ratio Test (Subspace GLRT) and the Adaptive Cosine Estimator (ACE), applied to a publicly-available AVIRIS datacube augmented with a synthetic material spectrum in selected pixels. The use of synthetic spectrum-augmented data enables quantitative comparison of Subspace-GLRT and ACE using Receiver Operating Characteristic (ROC) curves. For all cases investigated, Receiver Operating Characteristic (ROC) curves generated using ACE were as good as or superior to those generated using Subspace-GLRT. The favorability of ACE over Subspace-GLRT was more pronounced as the synthetic spectrum mixing fraction decreased. For probabilities of detection in the range of 50-80%, segmentation reduced the probability of false alarm by a factor of 3–5 when using ACE. In contrast, segmentation had no apparent effect on detection statistics using Subspace-GLRT, in this example.

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