Application of Artificial Intelligence in Nephropathology

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2	format correct	Conner Chen	+ 18 word(s)	3665	2022-09-06 08:45:02	\|

This entry is adapted from the peer-reviewed paper 10.3390/jcm11164918

Artificial intelligence (AI) and machine learning (ML) have triggered a vigorous technological revolution in the medical field. The use of AI algorithms provides cutting-edge guidance for clinical practice, including medical image analysis, smart diagnosis, curative effect evaluation, and prognosis prediction. AI technology is also regarded as a useful tool to improve the diagnostic efficiency and accuracy of renal pathologies.

machine learning artificial intelligence kidney diseases

1. Application of AI in Nephropathology

In recent years, chronic kidney disease (CKD) has become a major worldwide health issue with increasing incidence and prevalence. According to statistical reports, the global prevalence of CKD in 2019 was approximately 13.4%, and the number of patients with end-stage renal disease (ESRD) was estimated to be in the range from 4.902 to 7.803 million ^[1]. Therefore, early identification of disease etiology is recognized as a top priority for nephrologists to carry out targeted treatment and delay progression to CKD. To this end, as part of routine clinical practice, renal biopsy is an indispensable procedure that provides an objective basis for the determination of a definite diagnosis, prognosis, and appropriate treatment plan ^[2]. However, the current diagnosis made by renal pathology mainly depends on the assessment of a renal pathologist, which is not only time-consuming and labor-intensive but also involves subjectivity and relatively poor reproducibility ^[3]. Although pathologists receive the same systematic training and use the same standardized guidelines, diagnostic discrepancies still exist due to different visual perceptions, data processing habits, and judgment preference ^[4]. Artificial intelligence (AI)-based state-of-the-art technology might provide a possible solution for this problem. The various applications of AI algorithms in renal pathology are summarized in Table 1 and Table 2.

2. Detection and Segmentation of Kidney Structures

AI applications in the field of renal pathology started with imaging detection and segmentation of glomeruli because of their distinguishing features compared to other renal structures. Glomerular damage accounts for a substantial proportion of progressive CKD, leading to a decline in kidney function over time and even ESRD ^[5]. The number of normal glomeruli and the incidence of glomerulosclerosis are routinely assessed by pathological examination of kidney biopsies ^[6], indicating the risk of progression of glomerular diseases, such as glomerulonephritis and IgA nephropathy. However, a recent study (2016) showed that the number of glomeruli and the ratio of glomerulosclerosis measured by traditional light microscopy are inaccurate compared to those in whole-slide imaging (WSI) analysis. The errors were positively correlated with the sum of glomeruli. Therefore, the introduction of AI-assisted WSI analysis might be helpful to promote more precise evaluation of glomeruli ^[7].

In 2018, Simon et al. developed a support vector machine (SVM) model that could automatically identify normal glomeruli in mouse tissue samples. According to the image features provided by the local binary pattern (LBP), an effective texture descriptor for images, the model achieved a high accuracy of 90% and a recall rate of 70%. Moreover, it can be used for glomeruli detection in rat and human tissues regardless of staining conditions ^[8].

Apart from the identification of normal glomeruli, an AI application has also been reported in detecting glomerular lesions. Glomerular proliferative lesions, which are characterized by the increased number of cells in the glomeruli, mesangial area, or the capillary lumen, are considered the activity indicators for IgA nephropathy and lupus nephritis ^[9]^[10]. To identify abnormalities in glomerular proliferation, Chagas et al. proposed a new convolution neural network (CNN) network in a combination of SVM for the assessment of three sub-classifications of hypercellularity (mesangial proliferation, endocapillary hyperplasia, and mixed types) with an accuracy of 82% ^[11]. Moreover, glomerulosclerosis is characterized by sclerosis of various extents ranging from the segment to the entire glomerulus ^[12], indicating the extent of chronic kidney damage with a weak response to therapy of immunosuppression ^[13]. In kidney transplantation, the percentage of global glomerulosclerosis is also considered a determining factor in graft acceptance. Thus, there is a need to evaluate the status of global glomerulosclerosis carefully before implantation ^[14]. For example, a robust CNN network was established to segment and classify the various glomerular pathologic changes in the tissue slides from renal biopsies with diverse staining backgrounds. The image datasets in that study included 1123 snapshots and 348 WSIs. This network was trained to classify glomeruli into three categories, including normal glomeruli, sclerotic glomeruli, and glomeruli with other lesions. Using this network, the F1-score of the subgroups achieved 0.68–0.90 in the snapshot group, and the score reached a comparable level (0.75–0.83) in the WSI group ^[15]. In addition, new AI-assisted technique applications have been reported in recent studies, such as non-label classification and fine-grained characterization of glomerulosclerosis in renal biopsy pathological images ^[16]^[17].

Aside from global sclerosis and proliferative changes, AI-assisted identification of other lesions, such as crescents, also raised the interest of nephropathologists. To break the limitations of monotype change and to explore more types of pathological features, two CNN-based models were constructed, which can classify 12 and 9 pathological features of glomeruli, respectively. The performance of both models achieved moderate-to-high levels ^[18]^[19]. However, these two models are only suitable for analyzing the images of PAS-stained biopsies and may neglect certain characteristics shown by other staining methods. Therefore, to reach a more accurate and specific diagnosis, those images with different staining need to be merged for model training. Moreover, other types of images, such as immunofluorescence (IF) snapshots, have been suggested for integration. It has been reported that the appearance of immunoglobin deposition located in the area of the glomerular lesion can be automatically classified by deep learning approaches with high accuracy of more than 95% ^[20]. Thus, the combination of IF and light microscopic data might be realized soon.

In addition to the importance of glomeruli as an indispensable part of the kidney, pathological changes, such as developmental abnormalities, inflammation, and fibrosis, in other subtle structures including tubules and arteries can also be detected in digital images ^[21]. For example, the scores for interstitial inflammation, tubulitis, and intimal arteritis are included in the Banff classification reporting system, which is an international consensus system for renal allograft pathology evaluation ^[22]. A new trend in the field is to develop AI models to evaluate whole renal structures, not limited to glomeruli. One report focused directly on the segmentation of human kidney structures using 40 cases of PAS-stained WSIs made from kidney transplant biopsies for model training. The results revealed a high average Dice coefficient weighted by all classes of structure, regardless of the centers (0.80 and 0.84 on 2 datasets) and the source of samples (biopsy/nephrectomy) ^[3]. Further analysis showed that the AI’s ability to identify glomeruli, tubules, and interstitium of the kidney was top-ranked, while its ability to recognize atrophic tubules and empty Bowman’s capsule was less satisfying. Significant correlations were also found between quantifications of CNN segmentation and visually scored Banff classification, indicating the applicability of CNN in automatic routine evaluation for transplant kidney conditions.

Moreover, as a special “structure” in kidney, cancer masses can also be automatically classified and evaluated by grades with the help of deep learning algorithms. Fenstermaker et al. developed a CNN model based on H&E-stained WSIs from 42 renal cell carcinoma (RCC) specimens to distinguish normal tissue and 3 histology cancer subtypes including clear cell, chromophobe, and papillary carcinoma ^[23]. The accuracy of the model could reach as high as 99% in tumor tissue identification and 97.5% in RCC subtype classification. In addition, the model also predicted prognosis-associated Fuhrman grade according to nuclear size and polymorphism with a high accuracy of 98.4%. Thus, the results indicate that by highlighting the region of interest in advance and presenting their judgements for reference, artificial intelligence methods are expected to improve the accuracy and efficiency of kidney cancer diagnosis in the future.

3. Auxiliary Diagnosis of Renal Pathological Changes

3.1. Renal Interstitial Fibrosis

Renal interstitial fibrosis is the main pathological change in the period of end-stage renal failure, which is closely associated with the progression of various CKDs and the prognosis of kidney transplantation ^[24]. In addition, interstitial fibrosis (IF) and tubular atrophy (TA) are also regarded as the featured histologic changes of chronic allograft injury (CAI). The severity of CAI indicates a poor prognosis for renal allograft survival ^[25]. Therefore, early diagnosis and intervention of renal interstitial lesions are of great significance in delaying the loss of renal function.

Currently, the pathology of kidney biopsies is still a gold standard for diagnosing renal fibrosis ^[26]. Kidneys with interstitial fibrosis may have fibrous changes in different structures, such as the interstitium with remarkable inflammation, glomeruli with diffused fibrosis, atrophy tubules, and thickened renal arterioles ^[27]. However, due to large variations between different observers (e.g., reported κ-coefficient was 0.3), the reliability of the fibrosis evaluation remains a challenge ^[28]. Fortunately, computer-aided diagnosis tools can minimize observer bias because of their higher consistency, reproducibility, and standardization, as well as their ability to realize continuous quantification of fibrosis degree ^[29].

Ginley et al. reported a CNN model that was developed based on 116 WSIs ^[30]. With this model, the analysis results were not only close to the pathologist-determined scores of IF and TA but also significantly associated with patient outcomes. Recently, with advanced algorithms, the accuracy of AI in recognition of the finer structure and subtle changes in kidney biopsies has been improved, and the prediction power of allograft function has also been strengthened ^[31]^[32]. Thus, apart from the scoring of fibrosis extent, AI technologies may play a more crucial role in the prognosis and monitoring of post-transplant patients.

3.2. Lupus Nephritis

Lupus nephritis (LN) is characterized by the deposition of circulating or localized immune complexes in the kidneys ^[33]. Due to a deficiency in clinical manifestation of the pathological changes of LN, renal biopsies are recommended for all patients with LN to determine their pathological type ^[34]. The examination results of a renal biopsy are also closely associated with the formulation of immunotherapy regimens and a precise prognosis ^[35]. LN histology is routinely classified as Type I to VI based on the National Institutes of Health (NIH) Activity Index (NIH-AI) and NIH Chronicity Index (NIH-CI) for quantification of the degree of active inflammation and chronic changes as described in the International Society of Nephrology/Renal Pathology Society (ISN/RPS) 2018 classification ^[10]. However, some studies have pointed out that the classification criteria mentioned above were prone to interobserver variability with agreement ranging from poor to moderate ^[36]. Therefore, AI application tools can be adopted to improve the efficiency, objectivity, and accuracy of pathological diagnosis under the current guidelines.

A CNN model used to classify glomerular lesions in LN (impairments with slight/high severity or sclerosis) was developed by Zheng’s team using images obtained from 349 PAS-stained human kidney biopsies ^[37]. This model achieved a mean average precision of 0.807 at the glomerular level and attained a high concordance with the pathologist assessment at the kidney level (κ: 0.906). However, this model could only identify the most conspicuous lesions, which only account for a limited degree of LN pathological changes. Since some atypical LN lesions can be easily misclassified into other diseases, it is necessary to have a combinatorial assessment with other structural characteristics to distinguish the pathology.

Characteristic features of LN could also be identified using IF, including “full-house” staining and intensive C1q staining, as well as stained deposits outside the glomeruli or in the subendothelial and subepithelial layers ^[38]. Thus, some researchers tried to detect LN lesions in the IF background. For example, a multi-task learning (MTL) model was built to process IF images of four types of nephropathy ^[39]. The diagnosis of LN was improved by this model, with high accuracy of 0.91 and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.982, implying the promising potential for its clinical application in the future.

On the other hand, previous studies made attempts to integrate the baseline histopathological variables and laboratory data, which achieved remarkable advances in total accuracy and robustness ^[40]^[41]. Therefore, incorporating clinical indices into computer vision programs might overcome the limitations in the detection of LN lesions, thereby improving the accuracy of diagnosis and prognosis prediction.

3.3. Diabetic Nephropathy

Diabetic nephropathy (DN), as the principal microvascular complication of diabetes, has become the primary factor leading to ESRD ^[42]. The main pathological changes of DN in renal biopsy samples are the diffuse mesangial expansion in the early stage and Kimmelstiel–Wilson nodule formation with longer diabetic duration. The most significant and earliest change observed using electron microscopy (EM) is glomerular basement membrane (GBM) thickening ^[43]. Thus, a pathological classification was proposed, which was used to describe the progression stages of DN according to the characteristic glomerular lesions ^[44]. However, the classification based on visual assessments by different pathologists may produce varied results. To address this issue, more efficient tools to assess disease severity are needed.

To improve diagnostic reproducibility, one research team tried to combine image analysis with CNN algorithms for the classification of renal biopsy samples collected from 54 DN patients ^[45]. The agreement between the AI model and ground truth in the quantification and classification of DN lesions achieved a moderate level (Cohen’s kappa: 0.55). Although the results showed that the AI-assisted tool is currently unable to replace the function of human pathologists, it is notable that beneficial attempts have been made to overcome the subjectivity of artificial classification and improve the accuracy of clinical decision-making workflows in DN diagnosis.

In addition to AI application in lesion recognition of PAS-stained images, machine learning (ML) methods could detect pathological changes in IF images to produce encouraging results. Although immune complex deposition is considered unrelated to the main pathogenesis of DN, a previous study confirmed the value of IF images in the pathological diagnosis of DN. In that study, IF images of 885 renal biopsies, stained for IgG, IgM, IgA, C1q, C3, and fibrinogen, were used to construct deep learning programs ^[46], the results of which revealed the better performance of the AI-assisted technique (AUC 1.00) compared with human eye observation (AUC 0.75833). Further visualization and interpretation demonstrated the advantage of AI for surveying the surrounding areas of DN glomeruli, especially with regards to its potential to identify new important sub-visual changes that could not be found with the human eye alone.

Recently, DN diagnosis using EM has contributed to breakthroughs made by AI-assisted technology. For example, Hacking et al. designed a deep learning model (the MedKidneyEM-v1 Classifier) to classify five different renal lesions, including diabetic glomerulosclerosis ^[47]. As expected, the performance of this model was excellent for identifying DN, with an accuracy of 88.89% and a recall rate of 66.67%. Their pilot study not only confirmed the feasibility of the application of the deep learning model used for the analysis of EM images but also laid a technical foundation for the future development of AI-assisted EM models with optimized functions.

3.4. IgA Nephropathy

IgA nephropathy (IgAN) is currently the most common primary glomerular disease in European and Asian populations, and approximately 30% of patients with IgAN ultimately progress to ESRD within 20–25 years ^[48]^[49]. Under a light microscope, IgAN may present with various pathological features, such as hypercellularity, the proliferation of the mesangial matrix, focal necrosis, and segmental glomerulosclerosis ^[50].

In 2016, the International IgA Nephropathy Network and the Renal Pathology Society issued revised Oxford classification criteria that included hypercellularity in the mesangium (M), endocapillary proliferation (E), segmental sclerosis and adhesion (S), tubular atrophy, and interstitial fibrosis (T), as well as cellular/fibrocellular crescent formation (C) ^[51]. Although the predictive value of the Oxford classification or MEST-C score for IgAN has been verified by many clinical studies ^[52]^[53]^[54], the cumbersome requirements of the Oxford classification still cost pathologists much time and energy. Moreover, classifying the pathological conditions of the glomerulus following the Oxford classification could be difficult for clinicians, although it is an important determinant of treatment strategy ^[19]. Therefore, the application of AI-assisted tools in the quantitative analysis and automatic scoring of IgAN images might help relieve the burdens of pathologists and improve the accuracy of diagnosis.

In 2020, Zeng et al. developed algorithms that were used to identify glomerular lesions based on renal biopsy images collected from over 400 IgAN patients ^[55]. Like previously established networks, the new AI-assisted models can carry out multi-tasks, such as automatic localization of the glomeruli and classification of basic glomerular lesions related to IgAN pathological changes, including glomerular sclerosis, segmental sclerosis, and crescents. Analysis performed by those models achieved about 93.1% precision and 92.8% accuracy. Notably, these models can also accurately identify resident cells, such as mesangial cells, endothelial cells, and podocytes in the glomeruli and also generate the corresponding M-score according to the ratio of these cells, implying the innovative function of AI application in the analytic renal pathology system (ARPS).

To further clarify the link between glomerular lesions and clinical indicators, Sato et al. proposed an unsupervised model integrated with CNN and a visualization algorithm, which can perform cluster analysis in renal biopsy specimens collected from 68 IgAN patients ^[56]. This model could classify the glomeruli into 12 types and 10 patches, upon analysis of which the corresponding histological score for each glomerulus or patient was calculated. Their study confirmed the significant relationship between image-based scores and assessed clinical variables, although this new approach is not currently being applied in nephropathy. For instance, the defined sclerotic glomeruli were found to be associated with serum creatinine, systolic blood pressure, and urinary protein. These results not only provide visual interpretation for the previous findings according to the Oxford classification but also offer new insights into the relationship between pathological images and clinical variables ^[57].

Taken together, despite obvious progression in the diagnosis of IgAN with AI-assisted technology, there still exist several limitations in this field. The recognition scope is mainly restricted to glomerular lesions rather than the lesions involving the renal tubule and interstitium. Moreover, the methods for fine identification of histological structures, such as glomerular resident cells, are still in their infancy. Therefore, a deep investigation is required to improve the robustness and accuracy of these models. There are more requirements for realizing automatic Oxford scoring on all fronts. The summarization of auxiliary diagnosis via AI tools associated with specific kidney diseases is provided in Table 1.

Table 1. AI-assisted diagnosis of specific nephropathy.

Disease	Author	Year	Task	Methods	Slides	Main Results	Ref.
Renal interstitial fibrosis	Ginley et al.	2021	Detection and quantification of IFTA and glomerulosclerosis	CNN	116 WSIs, human (PAS)	High levels of agreement between CNN and four renal pathologists: IFTA agreement: ICC: 0.97 (0.94–0.99) glomerulosclerosis agreement: ICC: 0.91 (0.84–0.96)	^[30]
	Marechal et al.	2022	Automated segmentation of kidney tissue	CNN	241 samples of healthy kidney tissue, human	AUC: tubular atrophy: 0.92 interstitial fibrosis level: 0.91 vascular luminal stenosis (>50%): 0.85	^[31]
	Z. Yi et al.	2022	Recognition of interstitial fibrosis, tubular atrophy, and mononuclear leukocyte infiltration	U-Net and mask R-CNN algorithms	789 transplant biopsies, human (PAS)	Recognition of abnormal tubules: TPR: 84%	^[32]
	Farris et al.	2021	Quantification of interstitial fibrosis	VGG19 CNN	100 biopsy specimens, human	Moderate agreement between algorithm and pathologists: correlation coefficient: 0.46 (0.40–0.52)	^[58]
Lupus nephritis	Yang et al.	2021	Identification of glomerular lesion	ResNeXt-101	146 class III or IV (±class V) lupus nephritis biopsies, human (H&E)	Identification of globally sclerotic glomeruli: accuracy: 0.98–0.99 AUC of each kind of lesion: 0.687–0.946	^[59]
	Zheng et al.	2021	Classification of glomerular pathological findings in LN	YOLOv4 and VGG16	349 annotated WSIs (PAS) 321 unannotated WSIs (PAS)	Glomerular level: F1 (“slight” and “severe”): 0.924–0.952 Per-patient kidney level: weighted kappa with nephropathologist: 0.855	^[37]
	Pan et al.	2021	Classification of kidney diseases in IF images	AlexNet	655 IF images of IgAN (IF)	AUC of non-blurred IF images: 0.997 AUC of blurred IF images: 0.992	^[39]
	Cicalese et al.	2021	Classification of LGN	Uncertainty-guided Bayesian classification scheme	87 biopsy specimens, mice (PAS)	Weighted glomerular-level accuracy: 94.5%, weighted kidney-level accuracy: 96.6%	^[60]
Diabetic nephropathy	Ginley B et al.	2019	Classification of glomerular lesions	CNN	54 WSIs, human (PAS); 24 WSIs, mice (PAS)	Moderate Cohen’s kappa κ of agreement with a senior pathologist: 0.55 (0.40–0.60)	^[45]
	Kitamura S et al.	2020	Diagnosis of diabetic nephropathy with renal pathological immunofluorescence	Deep learning	885 renal immunofluorescent images, human	Six programs showed 100% accuracy, precision, and recall, and the AUC was 1.000	^[46]
	Hacking S et al.	2021	Classification of medical kidney disease on electron microscopy images	MedKidneyEM-v1 classifier (deep learning)	600 images	Diabetic glomerulosclerosis: precision: 88.89% recall: 66.67%	^[47]
	Ravi et al.	2019	Detection of glomerulosclerosis in DN	Genetic k-means	-	Detect 99% of pathological DN glomerulosclerosis	^[61]
IgA nephropathy	Zeng et al.	2020	Identification of glomerular lesions and intrinsic glomerular cell types	ARPS	400 WSIs, human (PAS)	Evaluation of global, segmental glomerular sclerosis, and crescents: Cohen’s kappa values: 1.0, 0.776, 0.861	^[55]
	Sato N et al.	2021	Evaluation of the relationship between kidney histological images and clinical information	CNN	68 WSIs, human (H&E)	Significant relationship between the score of the patch-based cluster containing crescentic glomeruli and SCr: coefficient = 0.09, p = 0.019	^[56]
	Purwar R et al.	2022	Detection of mesangial hypercellularity of MEST-C score	CNN	138 individual glomerulus images of IgA patients	Accuracy: 90 ± 2%, sensitivity: 90.4%, specificity: 80%	^[62]

Abbreviations: CNN: convolutional neural network; PAS: periodic acid–Schiff; IFTA: interstitial fibrosis, tubular atrophy; ICC: intraclass correlation coefficient; AUC: area under the curve; TPR: true positive rates; LGN: lupus glomerulonephritis; DN: diabetic nephropathy; ARPS: analytic renal pathology system.

4. Prognosis Prediction

Aside from its application in structure identification and auxiliary pathological diagnosis, AI-assisted technology can be applied in other tasks, such as prognosis prediction based on pathological images, risk stratification, and evaluation of therapeutic outcomes.

As an early stage exploration, Lee’s team used the unsupervised learning method to predict baseline and 1-year changes in the estimated glomerular filtration rate (eGFR). Based on the comprehensive morphological features extracted from 161 renal biopsies, along with patient clinical information, the AUC of the prediction model for eGFR at biopsy time reached 0.93, while that for 1-year eGFR was 0.80. These results indicated the potential of visual-feature-based algorithms for predicting CKD progression ^[63].

With regards to the prediction for specific kidney diseases such as interstitial fibrosis, Kolachalama et al. trained a CNN model to predict the renal survival rates at 1, 3, and 5 years based on the images of trichrome-stained renal biopsies with varying degrees of fibrosis. The AI-aided prediction tools achieved higher AUCs than did human pathologists, suggesting the feasibility of the image-based AI models in clinical decision-making augmentation ^[13]. The examples of digital image-based prediction for renal prognosis using AI algorithms are described in Table 2.

Table 2. Auxiliary prediction for prognosis.

Author	Year	Task	Methods	Slides	Main results	Ref.
Kolachalama et al.	2018	Prediction of the 1-, 3-, and 5-year renal survival rates	CNN	300 biopsies, human (trichrome-stain)	AUC of 1-, 3-, and 5-year renal survival: 0.878, 0.875, and 0.904	^[13]
Lee et al.	2022	Prediction of the baseline eGFR and 1-year change	ML	161 biopsies human (trichrome-stain)	AUC of baseline eGFR: 0.93, AUC of 1-year eGFR: 0.80	^[63]
Ledbetter et al.	2017	Prediction of 1-year eGFR	CNN	80 biopsies, human (trichrome-stain, PAS)	Mean absolute error of 17.55 mL/min	^[64]

Abbreviations: CNN: convolutional neural network; eGFR: estimated glomerular filtration rate; ML: machine learning.

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