Science

Application of artificial neural networks to clinical medicine

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A 51-year-old man walks into an emergency departmentwith mild left anterior chest pain. He denies anysignificant personal or family history of heart disease andhe has no other cardiac risk factors. Physical examinationreveals minimal bibasilar rates and is otherwise normal.An electrocardiogram (ECG) reveals slight T-waveflattening. The doctor then enters all the pertinentinformation accrued from the patient into the on-lineemergency department patient medical record andmanagement system. That system includes a neuralnetwork trained to identify acute myocardial infarction.The network analyses this information and relays the fact,via an on-screen window, that this patient has sustained amyocardial infarction. The physician combines thisinformation with his own impression and admits thepatient to the coronary-care unit. 4 h later cardiac enzymestudies confirm acute myocardial infarction.This scenario is based on a real network, representing

one of the first applications of artificial neural networks toclinical medicine. Preliminary studies have revealed thatthis network is more accurate than physicians inidentifying acute myocardial infarction in patientspresenting to the emergency department with anteriorchest pain.Many biological and pathophysiological processes

manifest "chaotic" behaviour. Chaotic processes are notbest analysed by classical linear methods. The first articlein this series, by Cross and colleagues, explained hownon-linear processing, as afforded by an artificial neuralnetwork, might improve upon the predictive power of theother approaches. The hope has been that the networksability to identify multidimensional relationships inclinical data not apparent to other forms of analyseswould allow the network to improve diagnostic accuracy.Non-linear statistical methods have been tried before butthey were complex and computationally intensive andnever became widely accepted. The advent of the artificialneural network and the much greater speed of todayscomputers have changed this.

Clinical diagnosis became one of the first areas towhich the artificial neural network was applied. Acutemyocardial infarction was one of the earliest applicationsbut the range is wide, from appendicitis to theexamination of biopsy specimens (panel 1). Starting withmyocardial infarction this review will cover some of thoseclinical applications. The use of neural networks in thelaboratory will be covered by Dybowski and Gant in thelast article in this Lancet series.2

Myocardial infarctionMyocardial infarction is especially challenging because itis a disease of low incidence yet a very high price has to bepaid for its misdiagnosis. As a result, physicians havetended to err on the side of safety: they push theirLancet 1995; 346: 1135-38

Department of Emergency Medicine, University of PennsylvaniaMedical Center, Philadelphia, PA 19104-4283, USA (W G Baxt MD)

diagnostic sensitivity as high as possible, leading to areduction in specificity and the unnecessary admission tohospital of significant numbers of patients who have nothad a myocardial infarction. A large study published in1988 put physicians sensitivity at 88% and specificity at71%."The first application of the artificial neural network to

chest pain appeared in 1989.12 This work trained amultilayer network on 174 patients presenting withanterior chest pain and it put patients into one of threediagnostic groups-high risk cardiac, low risk cardiac,and non-cardiac. However, these areas were not definedby any standard criteria. Another application was basedon a retrospective study of a set of 356 patients admittedto a cardiac intensive care unit. 120 had had a myocardialinfarction. The network was trained, using back-propagation, on half of the patients with and withoutmyocardial infarction, and it was tested on the remainingpatients, to whom the network had not been exposed.

Clinical pharmacologyPredicting: tumour sensitivity to drugs, patients 58-60response to warfarin, and central-nervous-system activity of alfentanil