A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides
Open accessAbstract
Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of renal allograft rejection. A fully automated system using a convolutional neural network (CNN) was developed to identify regions of interest (ROIs) and to detect C4d positive and negative peritubular capillaries (PTCs) in giga-pixel immunostained slides. The performance of faster R-CNN was evaluated using optimal parameters of the novel method to enlarge the size of labeled masks. Fifty and forty pixels of the enlarged size images showed the best performance in detecting C4d positive and negative PTCs, respectively. Additionally, the feasibility of deep-learning-assisted labeling as independent dataset to enhance detection in this model was evaluated. Based on these two CNN methods, a fully automated system for renal allograft rejection was developed. This system was highly reliable, efficient, and effective, making it applicable to real clinical workflow.
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In diabetic retinopathy diagnosis, CNN-based models have achieved performance on par with or even surpassing expert ophthalmologists.
“In DR diagnosis, CNN-based models have achieved performance on par with or even surpassing expert ophthalmologists [166, 167], overcoming the constraints of manual feature engineering.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [166]
The Introduction reports that a CNN trained on 130,000 fundus images performed comparably to expert ophthalmologists in diagnosing diabetic retinopathy. It does not report performance surpassing ophthalmologists. (LLM, conf=0.87)
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- Authors
- Kim, Y.-G., Choi, G., Go, H., Cho, Y., Lee, H., Lee, A.-R., Park, B., & Kim, N.
- Affiliations
- Seoul National University (KR)
- Asan Medical Center (KR)
- Corresponding author
- Go, Heounjeong
- Kim, Namkug
- ORCID
- Venue
- Scientific Reports
- Published
- 2019-03-26
- Volume
- 9
- Issue
- 1
- Pages
- 5123-5123
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6435691
- OpenAlex
- W2923883988
- Cited by
- 29
- References
- 27
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 6 · 2023: 2 · 2022: 6 · 2021: 6 · 2020: 4
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Asan Institute for Life Sciences, Asan Medical Center (2017-7028)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- renal allograft rejection, C4d staining, peritubular capillaries, automated detection, convolutional neural networks, Faster R-CNN, region of interest detection, deep learning, kidney transplantation
- MeSH terms
- Female, Graft Rejection, Humans, Image Processing, Computer-Assisted, Immunohistochemistry, Male, Kidney Transplantation, Neural Networks, Computer, Allografts
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