scholarly journals Real-Time Surgical Problem Detection and Instrument Tracking in Cataract Surgery

2020 ◽  
Vol 9 (12) ◽  
pp. 3896
Author(s):  
Shoji Morita ◽  
Hitoshi Tabuchi ◽  
Hiroki Masumoto ◽  
Hirotaka Tanabe ◽  
Naotake Kamiura
Keyword(s):  
Cataract Surgery ◽  
Real Time ◽  
False Positive Rate ◽  
Surgical Techniques ◽  
Surgical Skill ◽  
Skill Levels ◽  
Detection Rates ◽  
Surgical Methods ◽  
Instrument Tracking ◽  
Positive Rate

Surgical skill levels of young ophthalmologists tend to be instinctively judged by ophthalmologists in practice, and hence a stable evaluation is not always made for a single ophthalmologist. Although it has been said that standardizing skill levels presents difficulty as surgical methods vary greatly, approaches based on machine learning seem to be promising for this objective. In this study, we propose a method for displaying the information necessary to quantify the surgical techniques of cataract surgery in real-time. The proposed method consists of two steps. First, we use InceptionV3, an image classification network, to extract important surgical phases and to detect surgical problems. Next, one of the segmentation networks, scSE-FC-DenseNet, is used to detect the cornea and the tip of the surgical instrument and the incisional site in the continuous curvilinear capsulorrhexis, a particularly important phase in cataract surgery. The first and second steps are evaluated in terms of the area under curve (i.e., AUC) of the figure of the true positive rate versus (1—false positive rate) and the intersection over union (i.e., IoU) obtained by the ground truth and prediction associated with the region of interest. As a result, in the first step, the network was able to detect surgical problems with an AUC of 0.97. In the second step, the detection rate of the cornea was 99.7% when the IoU was 0.8 or more, and the detection rates of the tips of the forceps and the incisional site were 86.9% and 94.9% when the IoU was 0.1 or more, respectively. It was thus expected that the proposed method is one of the basic techniques to achieve the standardization of surgical skill levels.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

scholarly journals Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Hai Wang ◽  
Yingfeng Cai ◽  
Xiaobo Chen ◽  
Long Chen
Keyword(s):  
Detection Rate ◽  
False Positive Rate ◽  
Infrared Image ◽  
Far Infrared ◽  
Night Vision ◽  
Infrared Sensors ◽  
Night Time ◽  
Detection Rates ◽  
Positive Rate ◽  
And Performance

The use of night vision systems in vehicles is becoming increasingly common. Several approaches using infrared sensors have been proposed in the literature to detect vehicles in far infrared (FIR) images. However, these systems still have low vehicle detection rates and performance could be improved. This paper presents a novel method to detect vehicles using a far infrared automotive sensor. Firstly, vehicle candidates are generated using a constant threshold from the infrared frame. Contours are then generated by using a local adaptive threshold based on maximum distance, which decreases the number of processing regions for classification and reduces the false positive rate. Finally, vehicle candidates are verified using a deep belief network (DBN) based classifier. The detection rate is 93.9% which is achieved on a database of 5000 images and video streams. This result is approximately a 2.5% improvement on previously reported methods and the false detection rate is also the lowest among them.

Letters to the Editor

PEDIATRICS ◽  
1981 ◽  
Vol 68 (1) ◽  
pp. 144-145
Author(s):  
Lachlan Ch De Crespigny ◽  
Hugh P. Robinson
Keyword(s):  
Real Time ◽  
Linear Array ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Letters To The Editor ◽  
Premature Babies ◽  
Computed Tomography Scans ◽  
Positive Rate ◽  
Time Linear

We read with interest the report which suggested that the diagnosis of cerebroventricular hemorrhage ([CVH] including both subependymal [SEH] and intraventricular) with real time ultrasound was unreliable.1 Ultrasound, when compared with computed tomography scans, had a 35% false-positive rate and a 21% false-negative rate. In our institution over a 12-month period more than 200 premature babies have been examined (ADR real time linear array scanner with a 7-MHz transducer).

Association Rule-Mining-Based Intrusion Detection System With Entropy-Based Feature Selection

2020 ◽  
pp. 1-24
Author(s):  
Devaraju Sellappan ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Intrusion Detection ◽  
Association Rule ◽  
Intrusion Detection System ◽  
Association Rule Mining ◽  
Detection System ◽  
False Positive Rate ◽  
Rule Mining ◽  
Detection Rates ◽  
Mining Algorithm ◽  
Positive Rate

Intrusion detection system (IDSs) are important to industries and organizations to solve the problems of networks, and various classifiers are used to classify the activity as malicious or normal. Today, the security has become a decisive part of any industrial and organizational information system. This chapter demonstrates an association rule-mining algorithm for detecting various network intrusions. The KDD dataset is used for experimentation. There are three input features classified as basic features, content features, and traffic features. There are several attacks are present in the dataset which are classified into Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). The proposed method gives significant improvement in the detection rates compared with other methods. Association rule mining algorithm is proposed to evaluate the KDD dataset and dynamic data to improve the efficiency, reduce the false positive rate (FPR) and provides less time for processing.

Impact of Including or Removing Nuchal Translucency Measurement on the Detection and False-Positive Rates of First-Trimester Down Syndrome Screening

2015 ◽  
Vol 40 (3) ◽  
pp. 214-218 ◽  
Author(s):  
Emmanuel Spaggiari ◽  
Isabelle Czerkiewicz ◽  
Corinne Sault ◽  
Sophie Dreux ◽  
Armelle Galland ◽  
...  
Keyword(s):  
Down Syndrome ◽  
False Positive ◽  
False Positive Rate ◽  
Method Comparison ◽  
First Trimester ◽  
Nuchal Translucency ◽  
Routine Practice ◽  
Detection Rates ◽  
Significant Difference ◽  
Positive Rate

Introduction: First-trimester Down syndrome (DS) screening combining maternal age, serum markers (pregnancy-associated plasma protein-A and beta-human chorionic gonadotropin) and nuchal translucency (NT) gives an 85% detection rate for a 5% false-positive rate. These results largely depend on quality assessment of biochemical markers and of NT. In routine practice, despite an ultrasound quality control organization, NT images can be considered inadequate. The aim of the study was to evaluate the consequences for risk calculation when NT measurement is not taken into account. Material and Method: Comparison of detection and false-positive rates of first-trimester DS screening (PerkinElmer, Turku, Finland), with and without NT, based on a retrospective study of 117,126 patients including 274 trisomy 21-affected fetuses. NT was measured by more than 3,000 certified sonographers. Results: There was no significant difference in detection rates between the two strategies including or excluding NT measurement (86.7 vs. 81.8%). However, there was a significant difference in the false-positive rates (2.23 vs. 9.97%, p < 0.001). Discussion: Sonographers should be aware that removing NT from combined first-trimester screening would result in a 5-fold increase in false-positive rate to maintain the expected detection rates. This should be an incentive for maintaining quality in NT measurement.

scholarly journals Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation

2022 ◽  
Vol 12 (1) ◽  
pp. 415
Author(s):  
Vicente Quiles ◽  
Laura Ferrero ◽  
Eduardo Iáñez ◽  
Mario Ortiz ◽  
José M. Cano ◽  
...  
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
False Positive Rate ◽  
Experimental Session ◽  
Online Analysis ◽  
User Intention ◽  
Loop Control ◽  
Positive Ratio ◽  
Positive Rate ◽  
Speed Change

Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is proposed. This is carried out comparing three different classification models: monotonous walk vs. increasing and decreasing change speed intentions, monotonous walk vs. only increasing intention, and monotonous walk vs. only decreasing intention. The results indicate that, among the features tested, the most suitable parameter to represent these models are the Hjorth statistics in alpha and beta frequency bands. The average offline classification accuracy for the offline cross-validation of the three models obtained is 68 ± 11%. This selection is also tested following a pseudo-online analysis, simulating a real-time detection of the subject’s intentions to change speed. The average results indices of the three models during this pseudoanalysis are of a 42% true positive ratio and a false positive rate per minute of 9. Finally, in order to check the viability of the approach with an exoskeleton, a case of study is presented. During the experimental session, the pros and cons of the implementation of a closed-loop control of speed change for the H3 exoskeleton through EEG analysis are commented.

Association Rule-Mining-Based Intrusion Detection System With Entropy-Based Feature Selection

2021 ◽  
pp. 183-206
Author(s):  
Devaraju Sellappan ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Intrusion Detection ◽  
Association Rule ◽  
Intrusion Detection System ◽  
Association Rule Mining ◽  
Detection System ◽  
False Positive Rate ◽  
Rule Mining ◽  
Detection Rates ◽  
Mining Algorithm ◽  
Positive Rate

Intrusion detection system (IDSs) are important to industries and organizations to solve the problems of networks, and various classifiers are used to classify the activity as malicious or normal. Today, the security has become a decisive part of any industrial and organizational information system. This chapter demonstrates an association rule-mining algorithm for detecting various network intrusions. The KDD dataset is used for experimentation. There are three input features classified as basic features, content features, and traffic features. There are several attacks are present in the dataset which are classified into Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). The proposed method gives significant improvement in the detection rates compared with other methods. Association rule mining algorithm is proposed to evaluate the KDD dataset and dynamic data to improve the efficiency, reduce the false positive rate (FPR) and provides less time for processing.

Evaluation of an Immunomagnetic Separation–Real-Time PCR Assay for the Rapid Detection of Salmonella in Meat

2006 ◽  
Vol 69 (12) ◽  
pp. 2896-2901 ◽  
Author(s):  
ANGELIKA NOTZON ◽  
REINER HELMUTH ◽  
JOHANN BAUER
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Reference Method ◽  
Immunomagnetic Separation ◽  
Pcr Assay ◽  
Positive Rate ◽  
Meat Samples

The aim of this study was the comparison of an immunomagnetic separation (IMS)–real-time PCR assay for the detection of Salmonella with the cultural reference method according to §35 of the German Law on Food and Commodities (LMBG, L 00.00.20:1998). The IMS–real-time PCR assay includes a nonselective preenrichment step, an IMS, DNA extraction, as well as DNA purification followed by hybridization probe–based real-time PCR analysis. An accurate comparability was achieved, because both methods analyzed the same preenrichment. The evaluation was carried out using both artificially and naturally contaminated meat samples. The IMS–real-time PCR assay provides a result after 12 to 13 h. Compared with the reference method and regarding artificially contaminated meat samples, the IMS–real-time PCR assay achieved a specificity of 80% (false-positive rate of 20%) and a sensitivity of 100% (false-negative rate of 0%). The relative accuracy was 94%. The detection limit of both methods was 10 CFU/25 g. The concordance indexκ defines the statistical accordance, was 0.85 and indicated the agreement of both methods on statistical criteria. Compared to the reference method and analyzing naturally contaminated meat samples (n = 491), the IMS–real-time PCR assay showed a specificity of 99.3% (false-positive rate of 0.7%) and a sensitivity of 83.7% (false-negative rate of 16.3%). The relative accuracy was 98%. The concordance index κ had a value of 0.87 and highlighted the statistical agreement of both methods. In conclusion, the IMS–real-time PCR assay is suitable as specific, sensitive, and rapid screening method for the detection of Salmonella from meat.

The efficacy of combining several risk factors as a screening test

2005 ◽  
Vol 12 (4) ◽  
pp. 197-201 ◽  
Author(s):  
Nicholas J Wald ◽  
Joan K Morris ◽  
Simon Rish
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
False Positive ◽  
Detection Rate ◽  
False Positive Rate ◽  
Individual Risk ◽  
Detection Rates ◽  
Screening Performance ◽  
Positive Rate ◽  
The Individual

Objective: To determine the quantitative effect on overall screening performance (detection rate for a given false-positive rate) of using several moderately strong, independent risk factors in combination as screening markers. Setting: Theoretical statistical analysis. Methods: For the purposes of this analysis, it was assumed that all risk factors were independent, had Gaussian distributions with the same standard deviation in affected and unaffected individuals and had the same screening performance. We determined the overall screening performance associated with using an increasing number of risk factors together, with each risk factor having a detection rate of 10%, 15% or 20% for a 5% false-positive rate. The overall screening performance was estimated as the detection rate for a 5% false-positive rate. Results: Combining the risk factors increased the screening performance, but the gain in detection at a constant false-positive rate was relatively modest and diminished with the addition of each risk factor. Combining three risk factors, each with a 15% detection rate for a 5% false-positive rate, yields a 28% detection rate. Combining five risk factors increases the detection rate to 39%. If the individual risk factors have a detection rate of 10% for a 5% false-positive rate, it would require combining about 15 such risk factors to achieve a comparable overall detection rate (41%). Conclusion: It is intuitively thought that combining moderately strong risk factors can substantially improve screening performance. For example, most cardiovascular risk factors that may be used in screening for ischaemic heart disease events, such as serum cholesterol and blood pressure, have a relatively modest screening performance (about 15% detection rate for a 5% false-positive rate). It would require the combination of about 15 or 20 such risk factors to achieve detection rates of about 80% for a 5% false-positive rate. This is impractical, given the risk factors so far discovered, because there are too few risk factors and their associations with disease are too weak.

scholarly journals What We Think before a Voluntary Movement

2013 ◽  
Vol 25 (6) ◽  
pp. 822-829 ◽  
Author(s):  
Logan Schneider ◽  
Elise Houdayer ◽  
Ou Bai ◽  
Mark Hallett
Keyword(s):  
Real Time ◽  
False Positive ◽  
Voluntary Movement ◽  
False Positive Rate ◽  
Central Feature ◽  
Voluntary Movements ◽  
Eeg Signal ◽  
Positive Rate ◽  
Multiple Variables ◽  
The Brain

A central feature of voluntary movement is the sense of volition, but when this sense arises in the course of movement formulation and execution is not clear. Many studies have explored how the brain might be actively preparing movement before the sense of volition; however, because the timing of the sense of volition has depended on subjective and retrospective judgments, these findings are still regarded with a degree of scepticism. EEG events such as beta event-related desynchronization and movement-related cortical potentials are associated with the brain's programming of movement. Using an optimized EEG signal derived from multiple variables, we were able to make real-time predictions of movements in advance of their occurrence with a low false-positive rate. We asked participants what they were thinking at the time of prediction: Sometimes they were thinking about movement, and other times they were not. Our results indicate that the brain can be preparing to make voluntary movements while participants are thinking about something else.

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