Application Value of CTA in the Computer-Aided Diagnosis of Subarachnoid Hemorrhage of Different Origins

Subarachnoid hemorrhage (SAH) is difficult to detect because of its circulation through subarachnoid space, which leads to a high rate of missed diagnosis. Based on the above background, the purpose of this study is to study the application value of brain CT angiography (CTA) in computer-aided diagnosis of subarachnoid hemorrhage with a wide range of brain digital subtraction angiography as a gold standard. This paper collected images and related medical records of 111 patients with spontaneous subarachnoid hemorrhage receiving brain CTA and DSA examinations from February 2015 to November 2019 in the neurology department of our hospital. In contrast to the number, position, length, width, and neck width of the causative aneurysm detected by DSA, we evaluated the diagnostic results of CTA and evaluated whether there was statistical difference between the two detectives of intracranial aneurysms. The results showed that the area under ROC curve of subtraction CTA and conventional CTA was 1.000 and 0.818, respectively, which indicated that the former had better display effect on internal carotid aneurysm (AUC > 0.9), while the latter had medium value (0.7 < AUC ≤ 0.9), and the difference was statistically significant (z = 2.390, p = 0.017 ).

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A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

Behavioural Neurology ◽

10.1155/2021/2560388 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Hassaan Haider Syed ◽

Muhammad Attique Khan ◽

Usman Tariq ◽

Ammar Armghan ◽

Fayadh Alenezi ◽

...

Keyword(s):

Artificial Intelligence ◽

Computer Aided Diagnosis ◽

High Rate ◽

Ct Scans ◽

Computational Time ◽

False Alarms ◽

High Resolution Computed Tomography ◽

Computer Aided ◽

Aided Diagnosis

The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomography (CT) examinations conducted. The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec).

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Computer-aided Diagnosis and Medical Decision Support are not Synonymous

Methods of Information in Medicine ◽

10.1055/s-0038-1634612 ◽

1995 ◽

Vol 34 (04) ◽

pp. 369-370 ◽

Cited By ~ 2

Author(s):

F. T. de Dombal

Keyword(s):

Decision Support ◽

Computer Aided Diagnosis ◽

Medical Decision ◽

Medical Decision Support ◽

Computer Aided ◽

Aided Diagnosis

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Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

Methods of Information in Medicine ◽

10.1055/s-0038-1636061 ◽

1972 ◽

Vol 11 (01) ◽

pp. 32-37 ◽

Cited By ~ 10

Author(s):

F. T. DE DOMBAL ◽

J. C. HORROCKS ◽

J. R. STANILAND ◽

P. J. GUILLOU

Keyword(s):

Pattern Recognition ◽

Computer System ◽

Computer Aided Diagnosis ◽

Clinical Interview ◽

Computer Performance ◽

Pattern Size ◽

Computer Aided ◽

Computer Based ◽

Aided Diagnosis ◽

Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

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