Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones

Introduction: Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones. Methods: A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming “selfie” videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model’s performance for MRD1 estimation was evaluated on a separate test fold from the study dataset. Results: On the full test fold (N = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (r = 0.732). The mean absolute error was 0.822 mm; the mean of differences was −0.256 mm; and 95% limits of agreement (LOA) were −0.214–1.768 mm. Model performance showed no improvement when test data were gated to exclude “poor” quality images. Conclusions: On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (r = 0.732) between ground truth and predicted MRD1

Automated radiological analysis of spinal MRI

This thesis addresses the problem of analysing clinical MRI using modern computer vision methods for a variety of clinical and research-related tasks. We use automated machine learning algorithms to develop a spinal MRI analysis framework for a number of tasks such as vertebrae detection, labelling; disc and vertebrae segmentation, and radiological grading, and we validate the framework on a large, heterogeneous dataset of 300 symptomatic back pain patients from multiple clinical sites and scanners. Our framework has a number of back pain research and other spine-related clinical applications and could hopefully find application in a clinical workflow in the future. Our framework has five steps -- detection, labelling, segmentation, support regions and features, and machine learning for radiological measurements. The framework works in full 3D and has currently been implemented on sagittal T2 slices. We use Deformable Part Models along with a chain model to detect and label vertebrae, and a powerful graph cuts based method for vertebrae and disc segmentation. The labelled detections and segmentations are used to place support regions for feature extraction, which are mapped into a number of radiological measurements -- namely Pfirrmann grade, disc space narrowing, and herniation/bulge. The radiological ground truth was provided by a clinical radiologist with 25 years experience. We demonstrate a high performance in the measurement in each. The measurements are performed using support vector machines and support vector regressors learned on training data. We next investigate the problem of what is the best method of obtaining support regions. We first used pixel intensity features to predict the Pfirrmann grade, narrowing and bulge/herniation, with vertebrae segmentation to localise their support regions. Since segmentation of spine images, especially intervertebral discs is an unsolved problem and algorithms are prone to failure, we then ask the question, to segment or not to segment. To answer the question, we compare results on Pfirrmann grade prediction with three different points on the no segmentation to full disc segmentation involving no segmentation, vertebrae segmentation, or disc segmentation and find that vertebrae segmentation suffices. We finally show preliminary results in distinguishing between different radiological conditions related to the posterior side of the disc more finely than before in literature, taking information from both sagittal and axial slices to attempt to distinguish between herniated and bulged discs.</p

TinyMLOps : operational challenges for widespread edge AI adoption

Deploying machine learning applications on edge devices can bring clear benefits such as improved reliability, latency and privacy but it also introduces its own set of challenges. Most works focus on the limited computational resources of edge platforms but this is not the only bottleneck standing in the way of widespread adoption. In this paper we list several other challenges that a TinyML practitioner might need to consider when operationalizing an application on edge devices. We focus on tasks such as monitoring and managing the application, common functionality for a MLOps platform, and show how they are complicated by the distributed nature of edge deployment. We also discuss issues that are unique to edge applications such as protecting a model's intellectual property and verifying its integrity

ISSLS Prize in Bioengineering Science 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.

Study design Investigation of the automation of radiological features from magnetic resonance images (MRIs) of the lumbar spine. Objective To automate the process of grading lumbar intervertebral discs and vertebral bodies from MRIs. Summary of background data MR imaging is the most common imaging technique used in investigating low back pain (LBP). Various features of degradation, based on MRIs, are commonly recorded and graded, e.g., Modic change and Pfirrmann grading of intervertebral discs. Consistent scoring and grading is important for developing robust clinical systems and research. Automation facilitates this consistency and reduces the time of radiological analysis considerably and hence the expense. Methods 12,018 intervertebral discs, from 2009 patients, were graded by a radiologist and were then used to train: (1) a system to detect and label vertebrae and discs in a given scan, and (2) a convolutional neural network (CNN) model that predicts several radiological gradings. The performance of the model, in terms of class average accuracy, was compared with the intra-observer class average accuracy of the radiologist. Results The detection system achieved 95.6% accuracy in terms of disc detection and labeling. The model is able to produce predictions of multiple pathological gradings that consistently matched those of the radiologist. The model identifies ‘Evidence Hotspots’ that are the voxels that most contribute to the degradation scores. Conclusions Automation of radiological grading is now on par with human performance. The system can be beneficial in aiding clinical diagnoses in terms of objectivity of gradings and the speed of analysis. It can also draw the attention of a radiologist to regions of degradation. This objectivity and speed is an important stepping stone in the investigation of the relationship between MRIs and clinical diagnoses of back pain in large cohorts