Smartphone-based machine learning model for real-time assessment of medical kidney biopsy
Background: Kidney biopsy is the gold-standard for diagnosing medical renal diseases, but the accuracy of the diagnosis greatly depends on the quality of the biopsy specimen, particularly the amount of renal cortex obtained. Inadequate biopsies, characterized by insufficient cortex or predominant me...
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Elsevier
2024-12-01
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| Series: | Journal of Pathology Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353924000245 |
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| author | Odianosen J. Eigbire-Molen Clarissa A. Cassol Daniel J. Kenan Johnathan O.H. Napier Lyle J. Burdine Shana M. Coley Shree G. Sharma |
| author_facet | Odianosen J. Eigbire-Molen Clarissa A. Cassol Daniel J. Kenan Johnathan O.H. Napier Lyle J. Burdine Shana M. Coley Shree G. Sharma |
| author_sort | Odianosen J. Eigbire-Molen |
| collection | DOAJ |
| description | Background: Kidney biopsy is the gold-standard for diagnosing medical renal diseases, but the accuracy of the diagnosis greatly depends on the quality of the biopsy specimen, particularly the amount of renal cortex obtained. Inadequate biopsies, characterized by insufficient cortex or predominant medulla, can lead to inconclusive or incorrect diagnoses, and repeat biopsy. Unfortunately, there has been a concerning increase in the rate of inadequate kidney biopsies, and not all medical centers have access to trained professionals who can assess biopsy adequacy in real time. In response to this challenge, we aimed to develop a machine learning model capable of assessing the percentage cortex of each biopsy pass using smartphone images of the kidney biopsy tissue at the time of biopsy. Methods: 747 kidney biopsy cores and corresponding smartphone macro images were collected from five unused deceased donor kidneys. Each core was imaged, formalin-fixed, sectioned, and stained with Periodic acid–Schiff (PAS) to determine cortex percentage. The fresh unfixed core images were captured using the macro camera on an iPhone 13 Pro. Two experienced renal pathologists independently reviewed the PAS-stained sections to determine the cortex percentage. For the purpose of this study, the biopsies with less than 30% cortex were labeled as inadequate, while those with 30% or more cortex were classified as adequate. The dataset was divided into training (n=643), validation (n=30), and test (n=74) sets. Preprocessing steps involved converting High-Efficiency Image Container iPhone format images to JPEG, normalization, and renal tissue segmentation using a U-Net deep learning model. Subsequently, a classification deep learning model was trained on the renal tissue region of interest and corresponding class label. Results: The deep learning model achieved an accuracy of 85% on the training data. On the independent test dataset, the model exhibited an accuracy of 81%. For inadequate samples in the test dataset, the model showed a sensitivity of 71%, suggesting its capability to identify cases with inadequate cortical representation. The area under the receiver-operating curve (AUC-ROC) on the test dataset was 0.80. Conclusion: We successfully developed and tested a machine learning model for classifying smartphone images of kidney biopsies as either adequate or inadequate, based on the amount of cortex determined by expert renal pathologists. The model's promising results suggest its potential as a smartphone application to assist real-time assessment of kidney biopsy tissue, particularly in settings with limited access to trained personnel. Further refinements and validations are warranted to optimize the model's performance. |
| format | Article |
| id | doaj-art-6225d0513b144bf0b685563fa2f47928 |
| institution | OA Journals |
| issn | 2153-3539 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-6225d0513b144bf0b685563fa2f479282025-08-20T02:38:05ZengElsevierJournal of Pathology Informatics2153-35392024-12-011510038510.1016/j.jpi.2024.100385Smartphone-based machine learning model for real-time assessment of medical kidney biopsyOdianosen J. Eigbire-Molen0Clarissa A. Cassol1Daniel J. Kenan2Johnathan O.H. Napier3Lyle J. Burdine4Shana M. Coley5Shree G. Sharma6Arkana Laboratories, 10810 Executive Center Dr. Suite 100, Little Rock, AR 72211, USA; Corresponding author.Arkana Laboratories, 10810 Executive Center Dr. Suite 100, Little Rock, AR 72211, USAArkana Laboratories, 10810 Executive Center Dr. Suite 100, Little Rock, AR 72211, USAArkana Laboratories, 10810 Executive Center Dr. Suite 100, Little Rock, AR 72211, USADepartment of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USAArkana Laboratories, 10810 Executive Center Dr. Suite 100, Little Rock, AR 72211, USAArkana Laboratories, 10810 Executive Center Dr. Suite 100, Little Rock, AR 72211, USABackground: Kidney biopsy is the gold-standard for diagnosing medical renal diseases, but the accuracy of the diagnosis greatly depends on the quality of the biopsy specimen, particularly the amount of renal cortex obtained. Inadequate biopsies, characterized by insufficient cortex or predominant medulla, can lead to inconclusive or incorrect diagnoses, and repeat biopsy. Unfortunately, there has been a concerning increase in the rate of inadequate kidney biopsies, and not all medical centers have access to trained professionals who can assess biopsy adequacy in real time. In response to this challenge, we aimed to develop a machine learning model capable of assessing the percentage cortex of each biopsy pass using smartphone images of the kidney biopsy tissue at the time of biopsy. Methods: 747 kidney biopsy cores and corresponding smartphone macro images were collected from five unused deceased donor kidneys. Each core was imaged, formalin-fixed, sectioned, and stained with Periodic acid–Schiff (PAS) to determine cortex percentage. The fresh unfixed core images were captured using the macro camera on an iPhone 13 Pro. Two experienced renal pathologists independently reviewed the PAS-stained sections to determine the cortex percentage. For the purpose of this study, the biopsies with less than 30% cortex were labeled as inadequate, while those with 30% or more cortex were classified as adequate. The dataset was divided into training (n=643), validation (n=30), and test (n=74) sets. Preprocessing steps involved converting High-Efficiency Image Container iPhone format images to JPEG, normalization, and renal tissue segmentation using a U-Net deep learning model. Subsequently, a classification deep learning model was trained on the renal tissue region of interest and corresponding class label. Results: The deep learning model achieved an accuracy of 85% on the training data. On the independent test dataset, the model exhibited an accuracy of 81%. For inadequate samples in the test dataset, the model showed a sensitivity of 71%, suggesting its capability to identify cases with inadequate cortical representation. The area under the receiver-operating curve (AUC-ROC) on the test dataset was 0.80. Conclusion: We successfully developed and tested a machine learning model for classifying smartphone images of kidney biopsies as either adequate or inadequate, based on the amount of cortex determined by expert renal pathologists. The model's promising results suggest its potential as a smartphone application to assist real-time assessment of kidney biopsy tissue, particularly in settings with limited access to trained personnel. Further refinements and validations are warranted to optimize the model's performance.http://www.sciencedirect.com/science/article/pii/S2153353924000245Kidney biopsyDeep learningSmartphone imagingAdequacy assessment |
| spellingShingle | Odianosen J. Eigbire-Molen Clarissa A. Cassol Daniel J. Kenan Johnathan O.H. Napier Lyle J. Burdine Shana M. Coley Shree G. Sharma Smartphone-based machine learning model for real-time assessment of medical kidney biopsy Journal of Pathology Informatics Kidney biopsy Deep learning Smartphone imaging Adequacy assessment |
| title | Smartphone-based machine learning model for real-time assessment of medical kidney biopsy |
| title_full | Smartphone-based machine learning model for real-time assessment of medical kidney biopsy |
| title_fullStr | Smartphone-based machine learning model for real-time assessment of medical kidney biopsy |
| title_full_unstemmed | Smartphone-based machine learning model for real-time assessment of medical kidney biopsy |
| title_short | Smartphone-based machine learning model for real-time assessment of medical kidney biopsy |
| title_sort | smartphone based machine learning model for real time assessment of medical kidney biopsy |
| topic | Kidney biopsy Deep learning Smartphone imaging Adequacy assessment |
| url | http://www.sciencedirect.com/science/article/pii/S2153353924000245 |
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