Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis.

A system for analysis of histopathology data within a pharmaceutical R&D environment has been developed with the intention of enabling interdisciplinary collaboration. State-of-the-art AI tools have been deployed as easy-to-use self-service modules within an open-source whole slide image viewing platform, so that non-data scientist users (e.g., clinicians) can utilize and evaluate pre-trained algorithms and retrieve quantitative results. The outputs of analysis are automatically cataloged in the database to track data provenance and can be viewed interactively on the slide as annotations or heatmaps. Commonly used models for analysis of whole slide images including segmentation, extraction of hand-engineered features for segmented regions, and slide-level classification using multi-instance learning are included and new models can be added as needed. The source code that supports running inference with these models internally is backed up by a robust CI/CD pipeline to ensure model versioning, robust testing, and seamless deployment of the latest models. Examples of the use of this system in a pharmaceutical development workflow include glomeruli segmentation, enumeration of podocyte count from WT-1 immuno-histochemistry, measurement of beta-1 integrin target engagement from immunofluorescence, digital glomerular phenotyping from periodic acid-Schiff histology, PD-L1 score prediction using multi-instance learning, and the deployment of the open-source Segment Anything model to speed up annotation.

Automatic Deep Learning-assisted Detection and Grading of Abnormalities in Knee MRI Studies.

PURPOSE: To test the hypothesis that artificial intelligence (AI) techniques can aid in identifying and assessing lesion severity in the cartilage, bone marrow, meniscus, and anterior cruciate ligament (ACL) in the knee, improving overall MRI interreader agreement. MATERIALS AND METHODS: This retrospective study was conducted on 1435 knee MRI studies (n = 294 patients; mean age, 43 years ± 15 [standard deviation]; 153 women) collected within three previous studies (from 2011 to 2014). All MRI studies were acquired using high-spatial-resolution three-dimensional fast-spin-echo CUBE sequence. Three-dimensional convolutional neural networks were developed to detect the regions of interest within MRI studies and grade abnormalities of the cartilage, bone marrow, menisci, and ACL. Evaluation included sensitivity, specificity, and Cohen linear-weighted ĸ. The impact of AI-aided grading in intergrader agreement was assessed on an external dataset. RESULTS: Binary lesion sensitivity reported for all tissues was between 70% and 88%. Specificity ranged from 85% to 89%. The area under the receiver operating characteristic curve for all tissues ranged from 0.83 to 0.93. Deep learning-assisted intergrader Cohen ĸ agreement significantly improved in 10 of 16 comparisons among two attending physicians and two trainees for all tissues. CONCLUSION: The three-dimensional convolutional neural network had high sensitivity, specificity, and accuracy for knee-lesion-severity scoring and also increased intergrader agreement when used on an external dataset.Supplemental material is available for this article. Keywords: Bone Marrow, Cartilage, Computer Aided Diagnosis (CAD), Computer Applications-3D, Computer Applications-Detection/Diagnosis, Knee, Ligaments, MR-Imaging, Neural Networks, Observer Performance, Segmentation, Statistics © RSNA, 2021See also the commentary by Li and Chang in this issue.: An earlier incorrect version of this article appeared online. This article was corrected on April 16, 2021.

Erratum: Automatic Deep Learning-assisted Detection and Grading of Abnormalities in Knee MRI Studies.

[This corrects the article DOI: 10.1148/ryai.2021200165.].

Deep Learning for Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries from MRI.

PURPOSE: To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. MATERIALS AND METHODS: In this retrospective study, 1243 knee MR images (1008 intact, 18 partially torn, 77 fully torn, and 140 reconstructed ACLs) from 224 patients (mean age, 47 years ± 14 [standard deviation]; 54% women) were analyzed. The MRI examinations were performed between 2011 and 2014. A modified scoring metric was used. Classification of ACL injuries using deep learning involved use of two types of CNN, one with three-dimensional (3D) and the other with two-dimensional (2D) convolutional kernels. Performance metrics included sensitivity, specificity, weighted Cohen κ, and overall accuracy, and the McNemar test was used to compare the performance of the CNNs. RESULTS: The overall accuracies for ACL injury classification using the 3D CNN and 2D CNN were 89% (225 of 254) and 92% (233 of 254), respectively (P = .27), and both CNNs had a weighted Cohen κ of 0.83. The 2D CNN and 3D CNN performed similarly in classifying intact ACLs (2D CNN, sensitivity of 93% [188 of 203] and specificity of 90% [46 of 51] vs 3D CNN, sensitivity of 89% [180 of 203] and specificity of 88% [45 of 51]). Classification of full tears by both networks was also comparable (2D CNN, sensitivity of 82% [14 of 17] and specificity of 94% [222 of 237] vs 3D CNN, sensitivity of 76% [13 of 17] and specificity of 100% [236 of 237]). The 2D CNN classified all reconstructed ACLs correctly. CONCLUSION: Two-dimensional and 3D CNNs applied to ACL lesion classification had high sensitivity and specificity, suggesting that these networks could be used to help nonexperts grade ACL injuries. Supplemental material is available for this article. © RSNA, 2020.

Computer-Aided Detection AI Reduces Interreader Variability in Grading Hip Abnormalities With MRI.

BACKGROUND: Accurate interpretation of hip MRI is time-intensive and difficult, prone to inter- and intrareviewer variability, and lacks a universally accepted grading scale to evaluate morphological abnormalities. PURPOSE: To 1) develop and evaluate a deep-learning-based model for binary classification of hip osteoarthritis (OA) morphological abnormalities on MR images, and 2) develop an artificial intelligence (AI)-based assist tool to find if using the model predictions improves interreader agreement in hip grading. STUDY TYPE: Retrospective study aimed to evaluate a technical development. POPULATION: A total of 764 MRI volumes (364 patients) obtained from two studies (242 patients from LASEM [FORCe] and 122 patients from UCSF), split into a 65-25-10% train, validation, test set for network training. FIELD STRENGTH/SEQUENCE: 3T MRI, 2D T2 FSE, PD SPAIR. ASSESSMENT: Automatic binary classification of cartilage lesions, bone marrow edema-like lesions, and subchondral cyst-like lesions using the MRNet, interreader agreement before and after using network predictions. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, area under curve (AUC), specificity and sensitivity, and balanced accuracy. RESULTS: For cartilage lesions, bone marrow edema-like lesions and subchondral cyst-like lesions the AUCs were: 0.80 (95% confidence interval [CI] 0.65, 0.95), 0.84 (95% CI 0.67, 1.00), and 0.77 (95% CI 0.66, 0.85), respectively. The sensitivity and specificity of the radiologist for binary classification were: 0.79 (95% CI 0.65, 0.93) and 0.80 (95% CI 0.59, 1.02), 0.40 (95% CI -0.02, 0.83) and 0.72 (95% CI 0.59, 0.86), 0.75 (95% CI 0.45, 1.05) and 0.88 (95% CI 0.77, 0.98). The interreader balanced accuracy increased from 53%, 71% and 56% to 60%, 73% and 68% after using the network predictions and saliency maps. DATA CONCLUSION: We have shown that a deep-learning approach achieved high performance in clinical classification tasks on hip MR images, and that using the predictions from the deep-learning model improved the interreader agreement in all pathologies. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:1163-1172.

Learning osteoarthritis imaging biomarkers from bone surface spherical encoding.

PURPOSE: To learn bone shape features from spherical bone map of knee MRI images using established convolutional neural networks (CNN) and use these features to diagnose and predict osteoarthritis (OA). METHODS: A bone segmentation model was trained on 25 manually annotated 3D MRI volumes to segment the femur, tibia, and patella from 47 078 3D MRI volumes. Each bone segmentation was converted to a 3D point cloud and transformed into spherical coordinates. Different fusion strategies were performed to merge spherical maps obtained by each bone. A total of 41 822 merged spherical maps with corresponding Kellgren-Lawrence grades for radiographic OA were used to train a CNN classifier model to diagnose OA using bone shape learned features. Several OA Diagnosis models were tested and the weights for each trained model were transferred to the OA Incidence models. The OA incidence task consisted of predicting OA from a healthy scan within a range of eight time points, from 1 y to 8 y. The validation performance was compared and the test set performance was reported. RESULTS: The OA Diagnosis model had an area-under-the-curve (AUC) of 0.905 on the test set with a sensitivity and specificity of 0.815 and 0.839. The OA Incidence models had an AUC ranging from 0.841 to 0.646 on the test set for the range from 1 y to 8 y. CONCLUSION: Bone shape was successfully used as a predictive imaging biomarker for OA. This approach is novel in the field of deep learning applications for musculoskeletal imaging and can be expanded to other OA biomarkers.