Introduction: Electron microscopy (EM) is a crucial technique for identifying and distinguishing the specific location of deposits within glomeruli. Manual classification of these deposit locations is not only time-consuming but also yields inconsistent results among pathologists. This study aimed to develop a deep learning-based platform to automatically classify the locations of electron-dense deposits in EM images of kidney biopsies. Methods: We retrospectively collected 4,303 EM images at magnifications of ×4,000 to ×8,000 from 1,039 kidney biopsies performed on native kidneys at the Renal Pathology of King Medical Diagnostics Center in Guangzhou between June 1, 2022, and July 2, 2022. EM images were independently evaluated by pathologists Zhu and Luo for electron-dense deposits, categorized into mesangial, subepithelial, intramembranous, and subendothelial if present. These evaluations served as ground truth for model training and evaluation. Of these, 3,443 EM images were allocated to the training group and 860 to the test group. The ResNet18 architecture was selected for our task. To evaluate the model’s classification capability, we created a binary classification model to identify the presence of deposits in EM images. Furthermore, we implemented a subnet classification network to predict the probability of mesangial, subepithelial, intramembranous, and subendothelial deposits. Four renal pathologists (two EM pathologists and two comprehensive renal pathologists) were invited to compare their agreement with the deep learning model. Comparing deep learning models against pathologists with Cohen’s Kappa and accuracy. Results: The deep learning model can accurately identify the presence of electron-dense deposits in EM images, with an area under the receiver operating characteristic curve (AUC) of 0.959 and an accuracy of 0.899. The classification subnet trained to identify mesangial, subepithelial, intramembranous, and subendothelial deposits yielded AUCs of 0.928, 0.987, 0.986, and 0.944, with accuracies of 0.880, 0.962, 0.959, and 0.883, respectively. Subepithelial and intramembranous deposits had near-perfect agreement, while mesangial and subendothelial deposits showed substantial agreement with the ground truth. The accuracy of deep learning models in assessing the presence and locations of deposits was lower than that of EM pathologists but higher than that of comprehensive renal pathologists. A web platform has been developed in which users can upload EM images of renal biopsies to receive probabilities regarding the four locations of deposits based on our algorithm. Conclusion: We successfully developed a web platform for the automated assessment of the locations of electron-dense deposits in kidney biopsy EM images. The performance of this model surpasses that of experienced comprehensive renal pathologists, offering an efficient and reliable tool.

1.
Zhang
L
,
Wang
F
,
Wang
L
,
Wang
W
,
Liu
B
,
Liu
J
, et al
.
Prevalence of chronic kidney disease in China: a cross-sectional survey
.
Lancet
.
2012
;
379
(
9818
):
815
22
.
2.
Foreman
KJ
,
Marquez
N
,
Dolgert
A
,
Fukutaki
K
,
Fullman
N
,
McGaughey
M
, et al
.
Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories
.
Lancet
.
2018
;
392
(
10159
):
2052
90
.
3.
Xu
X
,
Wang
G
,
Chen
N
,
Lu
T
,
Nie
S
,
Xu
G
, et al
.
Long-term exposure to air pollution and increased risk of membranous nephropathy in China
.
J Am Soc Nephrol
.
2016
;
27
(
12
):
3739
46
.
4.
Schnuelle
P
.
Renal biopsy for diagnosis in kidney disease: indication, technique, and safety
.
J Clin Med
.
2023
;
12
(
19
):
6424
.
5.
John
R
.
Heptinstall’s pathology of the kidney
.
J Clin Pathol
.
2015
;
68
(
3
):
252
.
6.
Zhang
PL
,
Kanaan
HD
.
Silva’s diagnostic renal pathology
.
Am J Surg Pathol
.
2017
(
8
):
1
.
7.
Zhang
J
,
Zhang
A
.
Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification
.
BMC Nephrol
.
2023
;
24
(
1
):
132
.
8.
Hamet
P
,
Tremblay
J
.
Artificial intelligence in medicine
.
Metabolism
.
2017
;
69s
:
S36
s40
.
9.
Avanzo
M
,
Wei
L
,
Stancanello
J
,
Vallières
M
,
Rao
A
,
Morin
O
, et al
.
Machine and deep learning methods for radiomics
.
Med Phys
.
2020
;
47
(
5
):
e185
202
.
10.
Tran
KA
,
Kondrashova
O
,
Bradley
A
,
Williams
ED
,
Pearson
JV
,
Waddell
N
.
Deep learning in cancer diagnosis, prognosis and treatment selection
.
Genome Med
.
2021
;
13
(
1
):
152
.
11.
Yang
Y
,
Guan
S
,
Ou
Z
,
Li
W
,
Yan
L
,
Situ
B
.
Advances in AI‐based cancer cytopathology
.
Interdiscip Med
.
2023
;
1
(
3
).
12.
Zhou
LQ
,
Wang
JY
,
Yu
SY
,
Wu
GG
,
Wei
Q
,
Deng
YB
, et al
.
Artificial intelligence in medical imaging of the liver
.
World J Gastroenterol
.
2019
;
25
(
6
):
672
82
.
13.
Miao
J
,
Huang
Y
,
Wang
Z
,
Wu
Z
,
Lv
J
.
Image recognition of traditional Chinese medicine based on deep learning
.
Front Bioeng Biotechnol
.
2023
;
11
:
1199803
.
14.
Yamaguchi
R
,
Kawazoe
Y
,
Shimamoto
K
,
Shinohara
E
,
Tsukamoto
T
,
Shintani-Domoto
Y
, et al
.
Glomerular classification using convolutional neural networks based on defined annotation criteria and concordance evaluation among clinicians
.
Kidney Int Rep
.
2021
;
6
(
3
):
716
26
.
15.
Ginley
B
,
Lutnick
B
,
Jen
K-Y
,
Fogo
AB
,
Jain
S
,
Rosenberg
A
, et al
.
Computational segmentation and classification of diabetic glomerulosclerosis
.
J Am Soc Nephrol
.
2019
;
30
(
10
):
1953
67
.
16.
Marsh
JN
,
Liu
TC
,
Wilson
PC
,
Swamidass
SJ
,
Gaut
JP
.
Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy specimens
.
JAMA Netw Open
.
2021
;
4
(
1
):
e2030939
.
17.
Uchino
E
,
Suzuki
K
,
Sato
N
,
Kojima
R
,
Tamada
Y
,
Hiragi
S
, et al
.
Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach
.
Int J Med Inform
.
2020
;
141
:
104231
.
18.
Hermsen
M
,
de Bel
T
,
den Boer
M
,
Steenbergen
EJ
,
Kers
J
,
Florquin
S
, et al
.
Deep learning-based histopathologic assessment of kidney tissue
.
J Am Soc Nephrol
.
2019
;
30
(
10
):
1968
79
.
19.
Hacking
S
,
Bijol
V
.
Deep learning for the classification of medical kidney disease: a pilot study for electron microscopy
.
Ultrastruct Pathol
.
2021
;
45
(
2
):
118
27
.
20.
Selvaraju
RR
,
Cogswell
M
,
Das
A
,
Vedantam
R
,
Parikh
D
,
Batra
D
.
Grad-CAM: visual explanations from deep networks via gradient-based localization
. In:
2017 IEEE international conference on computer vision (ICCV)
.
IEEE Computer Society
;
2017
. p.
618
26
.
21.
Ligabue
G
,
Pollastri
F
,
Fontana
F
,
Leonelli
M
,
Furci
L
,
Giovanella
S
, et al
.
Evaluation of the classification accuracy of the kidney biopsy direct immunofluorescence through convolutional neural networks
.
Clin J Am Soc Nephrol
.
2020
;
15
(
10
):
1445
54
.
22.
Govind
D
,
Becker
JU
,
Miecznikowski
J
,
Rosenberg
AZ
,
Dang
J
,
Tharaux
PL
, et al
.
PodoSighter: a cloud-based tool for label-free podocyte detection in kidney whole-slide images
.
J Am Soc Nephrol
.
2021
;
32
(
11
):
2795
813
.
23.
Feng
C
,
Liu
F
.
Artificial intelligence in renal pathology: current status and future
.
Biomol Biomed
.
2023
;
23
(
2
):
225
34
.
24.
Zeng
C
,
Nan
Y
,
Xu
F
,
Lei
Q
,
Li
F
,
Chen
T
, et al
.
Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning
.
J Pathol
.
2020
;
252
(
1
):
53
64
.
25.
Hou
J
,
Nast
CC
.
Artificial intelligence: the next frontier in kidney biopsy evaluation
.
Clin J Am Soc Nephrol
.
2020
;
15
(
10
):
1389
91
.
26.
Santo
BA
,
Rosenberg
AZ
,
Sarder
P
.
Artificial intelligence driven next-generation renal histomorphometry
.
Curr Opin Nephrol Hypertens
.
2020
;
29
(
3
):
265
72
.
27.
Huo
Y
,
Deng
R
,
Liu
Q
,
Fogo
AB
,
Yang
H
.
AI applications in renal pathology
.
Kidney Int
.
2021
;
99
(
6
):
1309
20
.
28.
Xie
G
,
Chen
T
,
Li
Y
,
Chen
T
,
Li
X
,
Liu
Z
.
Artificial intelligence in nephrology: how can artificial intelligence augment nephrologists’ intelligence
.
Kidney Dis
.
2020
;
6
(
1
):
1
6
.
29.
Jiang
L
,
Chen
W
,
Dong
B
,
Mei
K
,
Zhu
C
,
Liu
J
, et al
.
A deep learning-based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images
.
Am J Pathol
.
2021
;
191
(
8
):
1431
41
.
30.
Zhang
L
,
Li
M
,
Wu
Y
,
Hao
F
,
Wang
C
,
Han
W
, et al
.
Classification of renal biopsy direct immunofluorescence image using multiple attention convolutional neural network
.
Computer Methods Programs Biomed
.
2022
:
214
.
31.
Pan
S
,
Fu
Y
,
Chen
P
,
Liu
J
,
Liu
W
,
Wang
X
, et al
.
Multi-task learning-based immunofluorescence classification of kidney disease
.
Int J Environ Res Public Health
.
2021
;
18
(
20
):
10798
.
32.
Smerkous
D
,
Mauer
M
,
Tøndel
C
,
Svarstad
E
,
Gubler
M-C
,
Nelson
RG
, et al
.
Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases
.
Kidney Int
.
2024
;
105
(
1
):
165
76
.
You do not currently have access to this content.