Comparison of Pathologic Response Evaluation Systems After Neoadjuvant Chemotherapy in Breast Cancers: Correlation With Computer-Aided Diagnosis of MRI Features

2019 ◽  
Vol 213 (4) ◽  
pp. 944-952 ◽  
Author(s):  
Woo Jung Choi ◽  
Hak Hee Kim ◽  
Joo Hee Cha ◽  
Hee Jung Shin ◽  
Eun Young Chae
Keyword(s):  
Neoadjuvant Chemotherapy ◽  
Computer Aided Diagnosis ◽  
Pathologic Response ◽  
Breast Cancers ◽  
Response Evaluation ◽  
Evaluation Systems ◽  
Mri Features ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Detection of Mesial Temporal Lobe Epilepsy in MRI Sequence T2 Flair MRI Image Using Computer Aided Diagnosis (CAD)

2020 ◽  
Vol 202 ◽  
pp. 15010
Author(s):  
Redha Okta Silfina ◽  
Hermina Sukmaningtyas ◽  
Rini Indrati
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Predictive Value ◽  
Hippocampal Sclerosis ◽  
Mesial Temporal Lobe Epilepsy ◽  
Computer Aided Diagnosis ◽  
Mri Features ◽  
Computer Aided ◽  
Mesial Temporal Lobe ◽  
Aided Diagnosis

Epilepsy is a serious disorder in the brain. One of the most frequently found is temporal lobe epilepsy. This type of epilepsy is mainly caused by hippocampal sclerosis and treatment is often refractory so it needs surgery, this epilepsy is called mesial temporal lobe epilepsy (MTLE). MRI features for hippocampal sclerosis seen visually are a decrease in T1-weighted intensity and an increase in T2-weighted intensity. T2WI and T2 FLAIR are the sequences most often assessed for the diagnosis of hippocampal sclerosis. The assessment carried out by the practitioner to see the increase in intensity of the sequence is done visually. Visual assessment has flaws because of the limited vision and subjectivity of the practitioner, thereby producing several opinions to determine the level of intensity of the sequence. In this study a Computer Aided Diagnosis (CAD) method is proposed to assess quantitatively by assessing the intensity that exists in the FLAIR T2 sequence. This research uses Computer Aided Diagnosis (CAD) with computer programming, Image processing as a tool to find the intensity value and get a cut-off point value > 825, from this result then conduct a test by measuring the sensitivity value (90%), specificity (69%), positive predictive value (80%), negative predictive value (83%) and accuracy (81%). The of area under the curve is 0.8119, with the average ability to determine the pain is not sick is 0.71 -0.91. The results of this study indicate that Computer Aided Diagnosis (CAD) is able to detect hippocampal sclerosis in ELTM well.

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

1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
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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