Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

Validation Of Expert System Enhanced Deep Learning Algorithm For Automated Screening For COVID-Pneumonia On Chest X-Rays

Abstract

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.



For full paper: http://www.nature.com/articles/s41598-021-02003-w

Device for Assessing Knee Joint Dynamics During Magnetic Resonance Imaging

Device For Assessing Knee Joint Dynamics During Magnetic Resonance Imaging

Abstract

Background:

Knee assessment with and without load using magnetic resonance imaging (MRI) can provide information on knee joint dynamics and improve the diagnosis of knee joint diseases. Performing such studies on a routine MRI-scanner require a load-exerting device during scanning. There is a need for more studies on developing loading devices and evaluating their clinical potential.



Purpose:

Design and develop a portable and easy-to-use axial loading device to evaluate the knee joint dynamics during the MRI study.


Study Type:

Prospective study.


Subjects:

Nine healthy subjects.


Field Strength/Sequence:

A 0.25 T standing-open MRI and 3.0 T MRI. PD-T2-weighted FSE, 3D-fast-spoiled-gradient-echo, FS-PD, and CartiGram sequences.


Assessment:

Design and development of loading device, calibration of loads, MR safety assessment (using projectile angular displacement, torque, and temperature tests). Scoring system for ease of doing. Qualitative (by radiologist) and quantitative (using structural similarity index measure [SSIM]) image-artifact assessment. Evaluation of repeatability, comparison with various standing stances load, and loading effect on knee MR parameters (tibiofemoral bone gap [TFBG], femoral cartilage thickness [FCT], tibial cartilage thickness [TCT], femoral cartilage T2-value [FCT2], and tibia cartilage T2-value [TCT2]). The relative percentage change (RPC) in parameters due to the device load was computed.


Statistical Test:

Pearson’s correlation coefficient (r).



Results:


The developed device is conditional-MR safe (details in the manuscript and supplementary materials), 15 × 15 × 45 cm3 dimension, and <3 kg. The ease of using the device was 4.9/5. The device introduced no visible image artifacts, and SSIM of 0.9889 ± 0.0153 was observed. The TFBG intraobserver variability (absolute difference) was <0.1 mm. Interobserver variability of all regions of interest was <0.1 mm. The load exerted by the device was close to the load during standing on both legs in 0.25 T scanner with r > 0.9. Loading resulted in RPC of 1.5%–11.0%, 7.9%–8.5%, and −1.5% to 13.0% in the TFBG, FCT, and TCT, respectively. FCT2 and TCT2 were reduced in range of 1.5–2.7 msec and 0.5–2.3 msec due to load.



Data Conclusion:


The proposed device is conditionally MR safe, low cost (material cost < INR 6000), portable, and effective in loading the knee joint with up to 50% of body weight.



Evidence Level:


1


Technical Efficacy:


Stage 1



For full paper: http://https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.27877

Model for in-vivo estimation of stiffness of tibiofemoral joint using MR imaging and FEM analysis

Model For In-Vivo Estimation Of Stiffness Of Tibiofemoral Joint Using MR Imaging And FEM Analysis

Abstract

Background:

Appropriate structural and material properties are essential for finite-element-modeling (FEM). In knee FEM, structural information could extract through 3D-imaging, but the individual subject’s tissue material properties are inaccessible.


Purpose:

The current study\’s purpose was to develop a methodology to estimate the subject-specific stiffness of the tibiofemoral joint using finite-element-analysis (FEA) and MRI data of knee joint with and without load.


Methods:

In this study, six Magnetic Resonance Imaging (MRI) datasets were acquired from 3 healthy volunteers with axially loaded and unloaded knee joint. The strain was computed from the tibiofemoral bone gap difference (ΔmBGFT) using the knee MR images with and without load. The knee FEM study was conducted using a subject-specific knee joint 3D-model and various soft-tissue stiffness values (1 to 50 MPa) to develop subject-specific stiffness versus strain models.


Results:

Less than 1.02% absolute convergence error was observed during the simulation. Subject-specific combined stiffness of weight-bearing tibiofemoral soft-tissue was estimated with mean values as 2.40 ± 0.17 MPa. Intra-subject variability has been observed during the repeat scan in 3 subjects as 0.27, 0.12, and 0.15 MPa, respectively. All subject-specific stiffness-strain relationship data was fitted well with power function (R2 = 0.997).


Conclusion:

The current study proposed a generalized mathematical model and a methodology to estimate subject-specific stiffness of the tibiofemoral joint for FEM analysis. Such a method might enhance the efficacy of FEM in implant design optimization and biomechanics for subject-specific studies.

Trial registration The institutional ethics committee (IEC), Indian Institute of Technology, Delhi, India, approved the study on 20th September 2017, with reference number P-019; it was a pilot study, no clinical trail registration was recommended.



For full paper: http://link.springer.com/article/10.1186/s12967-021-02977-1

Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion

Modified Radial-Search Algorithm For Segmentation Of Tibiofemoral Cartilage In MR Images Of Patients With Subchondral Lesion

Abstract

Purpose:

The quantitative analysis of weight-bearing articular cartilage superficial to subchondral abnormality is important in osteoarthritis (OA) progression studies. The current study aimed to address the challenges of a semi-automatic segmentation of tibiofemoral cartilage in MR images of OA patient with and without subchondral bone abnormalities (SBA).

Methods:

In this study, knee MRI data [fat-suppressed proton density-weighted, multi-echo T2-weighted (CartiGram) images] of 29 OA patients, acquired at 3.0T MR scanner, were retrospectively collected. Out of 29 data, 9 had SBA in femur bone. Initially, a semi-automatic femur cartilage segmentation based on radial intensity search approach by Akhtar et al. was implemented in-house. This algorithm was considered as the radial-search method for further comparison. In this current study, the reported radial-search (RS)-based semi-automatic cartilage segmentation method was modified using thresholding, connected component labelling, convex-hull operation and spline-based curve fitting for the improved segmentation of tibiofemoral cartilage. Cartilage was manually segmented by two experienced radiologists, and inter-reader variability was estimated using coefficient of variation (CV). The segmentation results were validated using dice coefficient (DC), Jaccard coefficient (JC) and sensitivity index measurements.

Results:

DC values for segmented femur cartilage in patients with SBA were 64.6 ± 7.8% and 81.4 ± 2.8% using reported RS method and modified radial-search method, respectively. DC values for segmented femur cartilage in patients without SBA were 82.5 ± 4.5% and 84.8 ± 2.0% using RS method and modified radial method, respectively. Similarly, DC values for tibial cartilage in all OA patients were 80.4 ± 1.6% and 81.9 ± 2.4% using RS method and modified radial method, respectively. Similar segmentation results were also obtained from the T2-weighted images. Inter-reader variability result based on CV in femur cartilage was 3.40 ± 2.12% (without SBA) and 4.18 ± 3.18% (with SBA).

Conclusion:

In the current study, a semi-automated segmentation of tibiofemoral cartilage was presented. Modified radial-search approach can successfully segment tibiofemoral cartilage, and the results were tested and validated on knee MRI data of OA patients with and without SBA.

For full paper: http://https://link.springer.com/article/10.1007/s11548-020-02116-z

Automatic delineation of anterior and posterior cruciate ligaments by combining deep learning and deformable atlas based segmentation

Automatic Delineation Of Anterior And Posterior Cruciate Ligaments By Combining Deep Learning And Deformable Atlas Based Segmentation

Abstract



Quantitative evaluation of bones and ligaments around knee joint from magnetic resonance imaging (MRI) often requires the boundaries of selected structures to be manually traced using computer software. It may take several hours to delineate all structures of interest in a three-dimensional (3D) dataset used for the evaluation. Thus, providing automated tools, which can delineate knee anatomical structures can improve productivity and efficiency in radiology departments. In recent years, 3D deep convolutional neural networks (3D CNN) have been successfully used for segmentation of knee bones and cartilage. However, the key challenge is segmentation of the anterior cruciate ligament (ACL) and the posterior cruciate ligament (PCL), due to high variability of intensities in the areas of pathologies such as ligament tear. In this approach, an open source 3D CNN is adapted for segmentation of knee bones and ligaments in the knee MRI. The segmentation accuracy of ACL and PCL is improved further by atlas based segmentation technique. The atlas mask is non-rigidly aligned with the patient image based on composite of rigid and deformable vector field derived between the bone masks in the atlas and corresponding segmented bone masks in the patient image. The level set functions corresponding to particular objects of interest of the deformed atlas are used to refine segmentation of the corresponding objects in the patient image. The accuracy of the proposed method is assessed using Dice coefficient score for 50 manual segmentations of bone, cartilage and ligaments comprising of both normal and knee injury cases. Our results show that the proposed approach offers a viable alternative to manual contouring of knee MRI volume by a human reader with improved accuracy compared to the 3D CNN.



The full article can be viewed here: http://doi.org/10.1117/12.2512431