The detection of objects of interest in high-resolution digital pathological images is a key part of diagnosis and is a labor-intensive task for pathologists. In this paper, we describe a Faster R-CNN-based approach for the detection of glomeruli in multistained whole slide images (WSIs) of human renal tissue sections. Faster R-CNN is a state-of-the-art general object detection method based on a convolutional neural network, which simultaneously proposes object bounds and objectness scores at each point in an image. The method takes an image obtained from a WSI with a sliding window and classifies and localizes every glomerulus in the image by drawing the bounding boxes. We configured Faster R-CNN with a pretrained Inception-ResNet model and retrained it to be adapted to our task, then evaluated it based on a large dataset consisting of more than 33,000 annotated glomeruli obtained from 800 WSIs. The results showed the approach produces comparable or higher than average F-measures with different stains compared to other recently published approaches. This approach could have practical application in hospitals and laboratories for the quantitative analysis of glomeruli in WSIs and, potentially, lead to a better understanding of chronic glomerulonephritis.
Introduction Diagnosing renal pathologies is important for performing treatments. However, classifying every glomerulus is difficult for clinicians; thus, a support system, such as a computer, is required. This paper describes the automatic classification of glomerular images using a convolutional neural network (CNN). Method To generate appropriate labeled data, annotation criteria including 12 features (e.g., “fibrous crescent”) were defined. The concordance among 5 clinicians was evaluated for 100 images using the kappa (κ) coefficient for each feature. Using the annotation criteria, 1 clinician annotated 10,102 images. We trained the CNNs to classify the features with an average κ ≥0.4 and evaluated their performance using the receiver operating characteristic–area under the curve (ROC–AUC). An error analysis was conducted and the gradient-weighted class activation mapping (Grad-CAM) was also applied; it expresses the CNN’s focusing point with a heat map when the CNN classifies the glomerular image for a feature. Results The average κ coefficient of the features ranged from 0.28 to 0.50. The ROC–AUC of the CNNs for test data varied from 0.65 to 0.98. Among the features, “capillary collapse” and “fibrous crescent” had high ROC–AUC values of 0.98 and 0.91, respectively. The error analysis and the Grad-CAM visually showed that the CNN could not distinguish between 2 different features that had similar visual structures or that occurred simultaneously. Conclusion The differences in the texture or frequency of the co-occurrence between the different features affected the CNN performance; thus, to improve the classification accuracy, methods such as segmentation are required.
The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipelines showed a mean intersection over union (IoU) of 0.670 and 0.693 for five classes (i.e., background, Bowman’s space, glomerular tuft, crescentic, and sclerotic regions) against the WSI of its facility, and 0.678 and 0.609 against the WSI of the external facility. The multivariate analysis revealed that the predicted sclerotic regions, even those that were predicted by the external model, had a significant negative impact on the slope of the estimated glomerular filtration rate after biopsy. This is the first study to demonstrate that the quantified sclerotic regions that are predicted by an automated computational pipeline for the segmentation of the histopathological glomerular components on WSIs impact the prognosis of kidney function in patients with IgAN.
Background Falls may cause elderly people to be bedridden, requiring professional intervention; thus, fall prevention is crucial. The use of electronic health records (EHRs) is expected to provide highly accurate risk assessment and length-of-stay data related to falls, which may be used to estimate the costs and benefits of prevention. However, no studies to date have investigated the extent to which hospital stays could be shortened through fall avoidance resulting from the use of prediction tools. Objective We first estimated the extended length of hospital stay caused by falls among elderly inpatients. Next, we developed a model that predicts falls using clinical text as input and evaluated its accuracy. Finally, we estimated the potentially shortened hospital stay that would be made possible by appropriate interventions based on the prediction model. Methods Patients aged 65 years or older were selected as subjects, and the EHRs of 1728 falls and 70,586 nonfalls were subjected to analysis. The extended-stay lengths were estimated using propensity score matching of 49 associated variables. Bidirectional encoder representations from transformers and bidirectional long short-term memory methods were used to predict falls from clinical text. The estimated length of stay and the outputs of the prediction model were used to determine stay reductions. Results The extended length of hospital stay due to falls was estimated to be 17.8 days (95% CI 16.6-19.0), which dropped to 8.6 days when there were unobserved covariates at an odds ratio of 2.0. The accuracy of the prediction model was as follows: area under the receiver operating characteristic curve, 0.851; F-value, 0.165; recall, 0.737; precision, 0.093; and specificity, 0.839. When assuming interventions with 25% or 100% effectiveness against cases where the model predicted a fall, the stay reduction was estimated at 0.022 and 0.099 days/day, respectively. Conclusions The accuracy of the prediction model using clinical text is considered to be higher than the prediction accuracy of conventional assessments. However, our model’s precision remained low at 9.3%. This may be due, in part, to the inclusion of cases in which falls did not occur because of preventative interventions during hospitalization. Nonetheless, it is estimated that interventions for cases when falls were predicted will reduce medical costs by 886 Yen/day (~US $6.50/day) of intervention, even if the preventative effect is 25%. Limitations include the fact that these results cannot be extrapolated to short- or long-term hospitalization cases, and that this was a single-center study.
BACKGROUND Falls may cause elderly people to be bedridden, requiring professional intervention; thus, prevention is crucial. The use of electronic health records (EHRs) is expected to provide highly accurate risk assessment and length-of-stay data related to falls, which may be used to estimate the costs and benefits of prevention. However, no studies exist that investigate the extent to which hospital stays could be shortened through fall avoidance resulting from the use of prediction tools. OBJECTIVE First, the extended length of hospital stays caused by falls among elderly inpatients was estimated. Next, we developed a model that predicts falls by using clinical text as input and evaluated its accuracy. Finally, we estimated the potentially shortened hospital stay that would be made possible by appropriate interventions based on the prediction model. METHODS Patients 65 years or older were selected as subjects, and the EHRs of 1,728 falls and 70,586 non-falls were subjected to analysis. The extended stay lengths were estimated using the propensity score-matching of 49 associated variables. Bidirectional encoder representations from transformers and bidirectional Long Short-Term Memory (LSTM) methods were used to predict falls from clinical text. The estimated length of stay and the outputs of the prediction model were used to determine stay reductions. RESULTS The extended length of hospital stay due to falls was estimated to be 17.8 days (95% CI: 16.6–19.0), and it fell to 8.6 days when there were unobserved covariants at an odds ratio of 2.0. The accuracy of the prediction model was as follows: area under the receiver operating characteristic curve, 0.851; F-value, 0.165; recall, 0.737; precision, 0.093; and specificity, 0.839. When assuming interventions with 25 or 100% effectiveness against cases where the model predicted a fall, the stay reduction was estimated at 0.038 and 0.154 days/day, respectively. CONCLUSIONS The accuracy of the prediction model using clinical text is thought to be higher than the prediction accuracy of conventional assessments. However, our model’s precision remained low at 9.3%. This may be due, in part, to the inclusion of cases in which falls did not occur because of preventative interventions during hospitalization. Nonetheless, it is estimated that interventions for cases when falls were predicted will reduce medical costs by 1,520 yen/day of intervention, even if the preventative effect is 25%. Limitations include the fact that these results cannot be extrapolated to short- or long-term hospitalization cases, and that this was a single-center study.
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