The Prediction of Common Bile Duct Stones Using a Neural Network

Golub, Robert M.; Cantu, Roberto; Tan, Mark C.

doi:10.1016/s1072-7515(98)00241-5

Cited by 26 publications

(17 citation statements)

References 54 publications

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“…They reported that, although MRCP was highly accurate at identification of axial biliary stones, a clinical classification system had a similar degree of accuracy [19] . Other novel approaches, such as discriminant analysis function and artificial neural networks, have been used successfully to identify axial biliary tree stones in patients undergoing cholecystectomy [20,21] . The usefulness of such methods for assessing the probability of presence of CBD stones in patients with acute biliary pancreatitis remains to be investigated.…”

Section: Discussionmentioning

confidence: 99%

The Selective Use of Magnetic Resonance Cholangiopancreatography in the Imaging of the Axial Biliary Tree in Patients with Acute Gallstone Pancreatitis

et al. 2008

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Section: Discussionmentioning

confidence: 99%

The Selective Use of Magnetic Resonance Cholangiopancreatography in the Imaging of the Axial Biliary Tree in Patients with Acute Gallstone Pancreatitis

et al. 2008

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“…Recently, ANNs have become popular in medical diagnosis [11][12][13][14][15] . Published medical applications include: prediction of intensive care unit resource utilization [16] , prediction of survival after trauma [17] , prediction of the mortality risk after heart surgery [18] , estimation of pulmonary artery occlusion pressure [19] , quantification of the risk of malignancy for abnormal mammograms [20] , diagnosis of early myocardial infarction [21] , prediction of active pulmonary tuberculosis [22] , prediction of early outcomes after liver transplantation [23] , and prediction of common bile duct stones [24] . Theoretical work by Dawes [25] suggests that while clinicians are good at determining which variables best categorize a diagnosis, they may not be as skilled in assigning a relative weight to each characteristic.…”

Section: Discussionmentioning

confidence: 99%

Design of an Artificial Neural Network for Diagnosis of Facial Pain Syndromes

Limonadi

McCartney

Burchiel³

2006

Stereotact Funct Neurosurg

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A classification scheme for facial pain syndromes describing seven categories has previously been proposed. Based on this classification scheme and a binomial (yes/no) facial pain questionnaire, we have designed and trained an artificial neural network (ANN) and as an initial feasibility assessment of such an ANN system examined its ability to recognize and correctly diagnose patients with different facial pain syndromes. One hundred patients with facial pain were asked to respond to a facial pain questionnaire at the time of their initial visit. After interview, an independent diagnosis was assigned to each patient. The patients’ responses to the questionnaire and their diagnoses were input to an ANN. The ANN was able to retrospectively predict the correct diagnosis for 95 of 100 patients (95%), and prospectively determine a correct diagnosis of trigeminal neuralgia Type 1 with 84% sensitivity and 83% specificity in 43 new patients. The ability of the ANN to accurately predict a correct diagnosis for the remaining types of facial pain was limited by our clinic sample size and hence less exposure to those categories. This is the first demonstration of the utilization of an ANN to diagnose facial pain syndromes.

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“…This model which was subsequently validated in prospective studies had a diagnostic accuracy of 90%, with a sensitivity of 81% and specificity of 92%. Some of the other surgically relevant diagnostic applications of ANNs include abdominal pain and appendicitis, 16 retained common bile duct stones, 17 glaucoma, 18 and back pain. 19 ANNs have also been used in diagnosing cytological and histological specimens.…”

Section: Diagnosismentioning

confidence: 99%

Artificial intelligence in medicine

Ramesh

Kambhampati²,

Monson³

et al. 2004

Ann R Coll Surg Engl

699

396

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A rtificial intelligence (AI) is defined as 'a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behaviour, and with the creation of artefacts that exhibit such behaviour'.1 Aristotle attempted to formalise 'right thinking' (logic) through his syllogisms (a three part deductive reasoning). Much of the work in the modern era was inspired by this and the early studies on the operation of mind helped to establish contemporary logical thinking. Programs which enable computers to function in the ways, that make people seem intelligent are called artificial intelligent systems. The British mathematician Alan Turing (1950) was one of the founders of modern computer science and AI. He defined intelligent behaviour in a computer as the ability to achieve human-level performance in cognitive tasks, this later became popular as the 'Turing test'.2 Since the middle of the last century, researchers have explored the potential applications of intelligent techniques in every field of medicine.3,4 The application of AI technology in the field of surgery was first successively investigated by Gunn in 1976, when he explored the possibility of diagnosing acute abdominal pain with computer analysis. 5 The last two decades have seen a surge in the interest in medical AI.Modern medicine is faced with the challenge of acquiring, analysing and applying the large amount of knowledge necessary to solve complex clinical problems. The development of medical artificial intelligence has been related to the development of AI programs intended Artificial intelligence in medicine AN

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The Prediction of Common Bile Duct Stones Using a Neural Network

Cited by 26 publications

References 54 publications

The Selective Use of Magnetic Resonance Cholangiopancreatography in the Imaging of the Axial Biliary Tree in Patients with Acute Gallstone Pancreatitis

The Selective Use of Magnetic Resonance Cholangiopancreatography in the Imaging of the Axial Biliary Tree in Patients with Acute Gallstone Pancreatitis

Design of an Artificial Neural Network for Diagnosis of Facial Pain Syndromes

Artificial intelligence in medicine

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