Sci Data. 2024 Jul 11;11(1):761. doi: 10.1038/s41597-024-03592-7.

Chloe Martin-King ¹, Ali Nael ^2 3, Louis Ehwerhemuepha ^4 5 6, Blake Calvo ^4 5, Quinn Gates ^4 5, Jamie Janchoi ⁴, Elisa Ornelas ⁴, Melissa Perez ⁷, Andrea Venderby ^4 5, John Miklavcic ^5 8, Peter Chang ^9 10 11, Aaron Sassoon ², Kenneth Grant ^7 12

Author information

¹Research Institute, Children's Health Orange County (CHOC), Orange, CA, USA. chloe.martin.king@choc.org.

²Department of Pathology, CHOC, Orange, CA, USA.

³Department of Pathology, University of California-Irvine (UCI) Medical Center, Orange, CA, USA.

⁴Research Institute, Children's Health Orange County (CHOC), Orange, CA, USA.

⁵Schmid College of Science and Technology, Chapman University, Orange, CA, USA.

⁶Department of Statistics, UCI Donald Bren School of Information and Computer Sciences, Irvine, CA, USA.

⁷Department of Gastroenterology and Nutrition, CHOC, Orange, CA, USA.

⁸School of Pharmacy, Chapman University, Irvine, CA, USA.

⁹Center for Artificial Intelligence in Diagnostic Medicine (CAIDM), UCI, Irvine, CA, USA.

¹⁰Department of Radiological Sciences, UCI School of Medicine, Orange, CA, USA.

¹¹Department of Computer Science, UCI Donald Bren School of Information and Computer Sciences, Irvine, CA, USA.

¹²Department of Pediatrics, UCI School of Medicine, Orange, CA, USA.

Abstract

The incidence of inflammatory bowel disease (IBD) is increasing annually. Children with IBD often suffer significant morbidity due to physical and emotional effects of the disease and treatment. Corticosteroids, often a component of therapy, carry undesirable side effects with long term use. Steroid-free remission has become a standard for care-quality improvement. Anticipating therapeutic outcomes is difficult, with treatments often leveraged in a trial-and-error fashion. Artificial intelligence (AI) has demonstrated success in medical imaging classification tasks. Predicting patients who will attain remission will help inform treatment decisions. The provided dataset comprises 951 tissue section scans (167 whole-slides) obtained from 18 pediatric IBD patients. Patient level structured data include IBD diagnosis, 12- and 52-week steroid use and name, and remission status. Each slide is labelled with biopsy site and normal or abnormal classification per the surgical pathology report. Each tissue section scan from an abnormal slide is further classified by an experienced pathologist. Researchers utilizing this dataset may select from the provided outcomes or add labels and annotations from their own institutions.