TY - GEN
T1 - Application of artificial neural networks for diagnosing acute appendicitis
AU - Park, Sung Yun
AU - Lee, Sangjoon
AU - Jeong, Jae Hoon
AU - Kim, Sung Min
PY - 2014
Y1 - 2014
N2 - The purpose of this study is to develop an appendicitis diagnosis system, by using artificial neural networks (ANNs). Acute appendicitis is one of the most common surgical emergencies of the abdomen. Various methods have been developed to diagnose appendicitis, but these methods have not shown good performance in the Middle East and Asia, or even in the West. We used the structures of ANNs with 801 patients. These various structures are a multilayer neural network structure (MLNN), a radial basis function neural network structure (RBF), and a probabilistic neural network structure (PNN). The Alvarado clinical scoring system was used for comparison with the ANNs. The accuracy of MLNN, RBF, PNN, and Alvarado was 97.84%, 99.80%, 99.41% and 72.19%, respectively. The AUC of MLNN, RBF, PNN, and Alvarado was 0.985, 0.998, 0.993, and 0.633, respectively. The performance of ANNs was significantly better than the Alvarado clinical scoring system (P<0.001). The models developed to diagnose appendicitis using ANNs showed good performance. We consider that the developed models can help junior clinical surgeons diagnose appendicitis.
AB - The purpose of this study is to develop an appendicitis diagnosis system, by using artificial neural networks (ANNs). Acute appendicitis is one of the most common surgical emergencies of the abdomen. Various methods have been developed to diagnose appendicitis, but these methods have not shown good performance in the Middle East and Asia, or even in the West. We used the structures of ANNs with 801 patients. These various structures are a multilayer neural network structure (MLNN), a radial basis function neural network structure (RBF), and a probabilistic neural network structure (PNN). The Alvarado clinical scoring system was used for comparison with the ANNs. The accuracy of MLNN, RBF, PNN, and Alvarado was 97.84%, 99.80%, 99.41% and 72.19%, respectively. The AUC of MLNN, RBF, PNN, and Alvarado was 0.985, 0.998, 0.993, and 0.633, respectively. The performance of ANNs was significantly better than the Alvarado clinical scoring system (P<0.001). The models developed to diagnose appendicitis using ANNs showed good performance. We consider that the developed models can help junior clinical surgeons diagnose appendicitis.
KW - Abdomen
KW - Appendicitis
KW - Area under the ROC curve
KW - Artificial neural network
KW - Clinical scoring system
UR - http://www.scopus.com/inward/record.url?scp=84891081913&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMM.479-480.445
DO - 10.4028/www.scientific.net/AMM.479-480.445
M3 - Conference contribution
AN - SCOPUS:84891081913
SN - 9783037859476
T3 - Applied Mechanics and Materials
SP - 445
EP - 450
BT - Applied Science and Precision Engineering Innovation
T2 - International Applied Science and Precision Engineering Conference 2013, ASPEC 2013
Y2 - 18 October 2013 through 22 October 2013
ER -