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Cited by 37Artificial Intelligence in HealthcareRenal Transplantation Outcomes and TreatmentsAI in cancer detection

A Neural Network Approach to the Biopsy Diagnosis of Early Acute Renal Transplant Rejection

Furness, Levesley, Luo, Taub, Kazi, Bates, Nicholson·1999·Histopathology
DOI 10.1046/j.1365-2559.1999.035005461.x
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Abstract

AIMS: To develop and test a neural network to assist in the histological diagnosis of early acute renal allograft rejection. METHODS AND RESULTS: We used three sets of biopsies to train and test the network: 100 'routine' biopsies from Leicester; 21 selected difficult biopsies which had already been evaluated by most of the renal transplant pathologists in the UK, in a study of the Banff classification of allograft pathology and 25 cases which had been classified as 'borderline' according to the Banff classification in a review of transplant biopsies from Oxford. The correct diagnosis for each biopsy was defined by careful retrospective clinical review. Biopsies where this review did not provide a clear diagnosis were excluded. Each biopsy was graded for 12 histological features and the data was entered into a simple single layer perception network, designed using the MATLAB neural network toolbox. Results were compared with logistic regression using the same data, and with 'conventional' histological diagnosis. If the network was trained only with the 100 'routine' cases, its performance with either of the other sets was poor. However, if either of the 'difficult' sets was added to the training group, testing with the other 'difficult' group improved dramatically; 19 of the 21 'Banff' study cases were diagnosed correctly. This was achieved using observations made by a trainee pathologist. The result is better than was achieved by any of the many experienced pathologists who had previously seen these biopsies (maximum 18/21 correct), and is considerably better than that achieved by using logistic regression with the same data. CONCLUSION: A neural network can provide a considerable improvement in the diagnosis of early acute allograft rejection, though further development work will be needed before this becomes a routine diagnostic tool. The selection of cases used to train the network is crucial to the quality of its performance. There is scope to improve the system further by incorporating clinical information. Other related areas where this approach is likely to be of value are discussed.

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  • Furness et al. developed a machine learning algorithm that diagnosed acute kidney allograft rejection more accurately than expert pathologists.

    “More than 2 decades ago, Furness et al. developed a machine learning algorithm that diagnosed the acute kidney allograft rejection more accurately than expert pathologists (43).”

    From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [43]

  • The Furness et al. algorithm was not fully automated-it relied on manual extraction of pathological features from histological slides.

    “However, the algorithm was not fully automated—it relied on manual extraction of pathological features from histological slides.”

    From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [43]

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Authors
Furness, Levesley, Luo, Taub, Kazi, Bates, & Nicholson
Affiliations
  • Leicester General Hospital (GB)
  • University of Leicester (GB)
  • Sindh Institute of Urology and Transplantation (PK)
  • John Radcliffe Hospital (GB)
Corresponding author
Furness
Venue
Histopathology
Publisher
Wiley
Published
1999-11
Volume
35
Issue
5
Pages
461-467
ISSN
  • 0309-0167 (ISSN-L)
  • 1365-2559
DOI
10.1046/j.1365-2559.1999.035005461.x
OpenAlex
W2032819488
Cited by
37
References
16
Citations by year
2026: 1 · 2025: 3 · 2024: 1 · 2023: 3 · 2022: 2 · 2021: 1 · 2014: 2 · 2013: 1 · 2012: 2
FWCI
9.12
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 785 d)
Locations
  • Histopathology (publishedVersion)
  • PubMed (publishedVersion)
  • Leicester Research Archive (University of Leicester) (submittedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Transplantation › Renal Transplantation Outcomes and Treatments
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
acute renal allograft rejection, kidney transplantation, renal biopsy, computer-aided diagnosis, artificial neural networks, Banff classification, histopathological diagnosis, logistic regression, single-layer perceptron, training data selection
MeSH terms
Acute Disease, Biopsy, Evaluation Studies as Topic, Graft Rejection, Humans, Kidney, Reproducibility of Results, Logistic Models, Kidney Transplantation, Neural Networks, Computer

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