Developing a Neural Network Model for a Non-invasive Prediction of Histologic Activity in Inflammatory Bowel Diseases
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Abstract
Background: Colonoscopy with biopsy is the “gold” standard for evaluating disease activity in inflammatory bowel diseases (IBD). Current
research is geared toward finding non-invasive, cost-efficient methods that estimate disease activity. We aimed to develop a neural
network (NN) model for the non-invasive prediction of histologic activity in IBD using routinely available clinical-biological parameters.
Methods: Standard clinical-biological parameters and histologic activity from 371 ulcerative colitis (UC) and 115 Crohn’s disease (CD)
patient records were collected. A training set, a test set, and a validation set were used for building/validating 2 models for each disease.
All models had binary output predicting the active/inactive histologic disease status. For both diseases, the first model used both clinical
and biological inputs, while the second used only biological data.
Results: First UC model obtained an accuracy of 95.59% on the test set and 96.67% on the validation set. The second UC model
achieved accuracies of 88.24% and 86.67% on the test and validation sets, respectively. The First CD classifier resulted in 90.48%
accuracy on the test set and 91.67% on the validation set. Finally, the second CD classifier obtained an accuracy of 85.71% on the test
set and 91.67% on the validation set.
Conclusions: An accurate and non-invasive artificial intelligence system to predict histologic disease activity in IBD is designed. Our
models achieved similar or better results compared to the documented performance of fecal calprotectin (the best non-invasive IBD
biomarker to date). Given these favorable results, we anticipate the future utility in the clinical setting of a non-invasive disease activity
prediction.
research is geared toward finding non-invasive, cost-efficient methods that estimate disease activity. We aimed to develop a neural
network (NN) model for the non-invasive prediction of histologic activity in IBD using routinely available clinical-biological parameters.
Methods: Standard clinical-biological parameters and histologic activity from 371 ulcerative colitis (UC) and 115 Crohn’s disease (CD)
patient records were collected. A training set, a test set, and a validation set were used for building/validating 2 models for each disease.
All models had binary output predicting the active/inactive histologic disease status. For both diseases, the first model used both clinical
and biological inputs, while the second used only biological data.
Results: First UC model obtained an accuracy of 95.59% on the test set and 96.67% on the validation set. The second UC model
achieved accuracies of 88.24% and 86.67% on the test and validation sets, respectively. The First CD classifier resulted in 90.48%
accuracy on the test set and 91.67% on the validation set. Finally, the second CD classifier obtained an accuracy of 85.71% on the test
set and 91.67% on the validation set.
Conclusions: An accurate and non-invasive artificial intelligence system to predict histologic disease activity in IBD is designed. Our
models achieved similar or better results compared to the documented performance of fecal calprotectin (the best non-invasive IBD
biomarker to date). Given these favorable results, we anticipate the future utility in the clinical setting of a non-invasive disease activity
prediction.