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.

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

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

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.

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.

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.

Headspace analysis study of evaporation rate of perfume ingredients applied onto skin

Synopsis Diffusion of perfume ingredients from skin or hair is measured using an original method based on dynamic headspace technology. This has been used for pure odorants, fine fragrances, as well as for perfumed cosmetic applications such as soaps, creams or shampoos, in order to characterize diffusion processes and air/skin or air/hair partitioning. Accordingly, a special collection system, applied on the inner face of the forearm, has been developed, allowing the adsorption of diffusing organic vapours from skin onto Tenax (poly-diphenyl phenylene oxide) with a controlled air flow rate. A simple model composition containing eleven volatile synthetic odorants was prepared in an alcoholic matrix and the solution was applied onto the skin. The diffusion rate of the different components was measured by determining the concentration of each in the gas phase versus time. Conversely, the same experiment was effected by the application of an alcoholic solution of each individual component. In this manner, the relative diffusion from skin of the components alone or mixed was compared using the same experimental technique. The effect of a musky component was also tested. Both compositions (with and without musk) were then applied in a soap base. Thus, following a rigorous protocol, the forearm was washed with the perfumed soap and rinsed with water before collection of the headspace. The results show the different diffusion rates of the individual odorants. In particular, components evaporate slower from the skin when they have been applied from a soap bar compared to when they have been applied from alcoholic solution. We also present results describing the characterization of skin types using a panel comprised of 80 people (40 females and 40 males); amount of sebum, hydration and pH were systematically measured on different parts of the face, the neck as well as the outer and inner faces of the forearm. The panelists were then classified into different sub-groups taking into account these parameters. It should be noted that the foregoing results were obtained on an 'average'skin type.

Improved hardware and software for quick gas chromatography-olfactometry using CHARM and GC-"SNIF" analysis

A multisniffing system has been developed to allow three panelists to simultaneously participate in a GC-olfactometric analysis. This device, associated with a computerized data treatment, allows shortening CHARM and GC-"SNIF' analyses to less than 1 week and less than 1 day, respectively. The program was developed as an extension of an existing commercial chromatography data system, as usual GC processing functions are suited to the treatment of olfactograms (plots of odor response versus GC elution time). Because of the improved algorithm, the consequences of gaps in coincident responses were minimized, and the systematic use of a panel improved the repeatability of CHARM olfactograms. Comparing both methods, GC-SNIF repeatability appears to be higher than that of CHARM, as the former method uses a larger panel, but in a shorter lapse of time.