Background Mobile health (mHealth) offers potential benefits to both patients and healthcare systems. Existing remote technologies to measure respiratory rates have limitations such as cost, accessibility and reliability. Using smartphone sensors to measure respiratory rates may offer a potential solution to these issues. Objective The aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure respiratory rates using movement sensors. Methods In Study 1, 15 participants simultaneously measured their respiratory rates with the app and a Food and Drug Administration-cleared reference device. A novel reference analysis method to allow the app to be evaluated ‘in the wild’ was also developed. In Study 2, 165 participants measured their respiratory rates using the app, and these measures were compared to the novel reference. The usability of the app was also assessed in both studies. Results The app, when compared to the Food and Drug Administration-cleared and novel references, respectively, showed a mean absolute error of 1.65 ( SD = 1.49) and 1.14 (1.44), relative mean absolute error of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement = –3.27 to 4.89) and 0.08 (–3.68 to 3.51). Pearson correlation coefficients were 0.700 and 0.885. Ninety-three percent of participants successfully operated the app on their first use. Conclusions The accuracy and usability of the app demonstrated here in individuals with a normal respiratory rate range show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor respiratory rates. Further research should validate the benefits that this technology may offer patients and healthcare systems.

The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

Mobile health (mHealth) offers notable potential clinical and economic benefits to patients and healthcare systems alike. Although respiratory rate (RR) is of great clinical significance, existing remote technologies to measure RR suffer from limitations, such as cost, accessibility and reliability. Using smartphone movement sensors to measure RR may offer a potential solution to these shortcomings. The aim of this study was to conduct a comprehensive and ecologically valid assessment of a novel mHealth smartphone application designed to measure RR using movement sensors. Study 1 offered a preliminary evaluation, in which RR measurements from 15 participants generated via the mHealth app were compared to simultaneous measurements from a reference device cleared by the US Food and Drug Administration (FDA). Participants’ ability to successfully operate the app was also determined. Finally, a novel reference method, that would allow accuracy of the mHealth app to be investigated ‘in the wild’, was assessed for validity against the FDA-cleared reference. In Study 2, 165 participants of balanced demographics remotely downloaded the mHealth app and measured their RR. Measures from the mHealth app were compared to the novel reference that was assessed in Study 1. Usability was quantified based on the proportion of participants that were able to successfully use the app to measure their RR and standardised usability scales. Outcomes from Study 1 supported further assessment of the mHealth app, including as assessed by the novel reference. The mHealth app, when compared to the FDA-cleared and novel references, respectively, showed a mean absolute error (MAE) of 1.65 (standard deviation (SD) = 1.49) and 1.14 (1.44), relative MAE of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement (LoA) = -3.27-4.89) and 0.08 (-3.68-3.51). Pearson Product Moment Correlation (PPMC) coefficients were 0.700 and 0.885. 93% of participants could successfully operate the device on their first use and standardised usability scores were above industry averages. The accuracy and usability of the mHealth app demonstrated in this research hold promise for the use of mHealth solutions employing smartphone movement sensors to remotely monitor RR. Considering methodological limitations, further research should be undertaken to more holistically validate the benefits that this technology may offer patients and healthcare systems.

OBJECTIVE:We describe and evaluate an automated software tool for nerve-fiber detection and quantification in corneal confocal microscopy (CCM) images, combining sensitive nerve- fiber detection with morphological descriptors.METHOD:We have evaluated the tool for quantification of Diabetic Sensorimotor Polyneuropathy (DSPN) using both new and previously published morphological features. The evaluation used 888 images from 176 subjects (84 controls and 92 patients with type 1 diabetes). The patient group was further subdivided into those with ( n = 63) and without ( n = 29) DSPN.RESULTS:We achieve improved nerve- fiber detection over previous results (91.7% sensitivity and specificity in identifying nerve-fiber pixels). Automatic quantification of nerve morphology shows a high correlation with previously reported, manually measured, features. Receiver Operating Characteristic (ROC) analysis of both manual and automatic measurement regimes resulted in similar results in distinguishing patients with DSPN from those without: AUC of about 0.77 and 72% sensitivity-specificity at the equal error rate point.CONCLUSION:Automated quantification of corneal nerves in CCM images provides a sensitive tool for identification of DSPN. Its performance is equivalent to manual quantification, while improving speed and repeatability.SIGNIFICANCE:CCM is a novel in vivo imaging modality that has the potential to be a noninvasive and objective image biomarker for peripheral neuropathy. Automatic quantification of nerve morphology is a major step forward in the early diagnosis and assessment of progression, and, in particular, for use in clinical trials to establish therapeutic benefit in diabetic and other peripheral neuropathies.

A proof of concept is presented for cross-modality anatomical landmark detection using histograms of unsigned gradient orientations (HUGO) as machine learning image features. This has utility since an existing algorithm trained on data from one modality may be applied to data of a different modality, or data from multiple modalities may be pooled to train one modality-independent algorithm. Landmark detection is performed using a random forest trained on HUGO features and atlas location autocontext features. Three-way cross-modality detection of 20 landmarks is demonstrated in diverse cohorts of CT, MRI T1 and MRI T2 scans of the head. Each cohort is made up of 40 training and 20 test scans, making 180 scans in total. A cross-modality mean landmark error of 5.27 mm is achieved, compared to single-modality error of 4.07 mm.

Neuropad is currently a categorical visual screening test that identifies diabetic patients at risk of foot ulceration. The diagnostic performance of Neuropad was compared between the categorical and continuous (image-analysis (Sudometrics)) outputs to diagnose diabetic peripheral neuropathy (DPN). 110 subjects with type 1 and 2 diabetes underwent assessment with Neuropad, Neuropathy Disability Score (NDS), peroneal motor nerve conduction velocity (PMNCV), sural nerve action potential (SNAP), Deep Breathing-Heart Rate Variability (DB-HRV), intraepidermal nerve fibre density (IENFD), and corneal confocal microscopy (CCM). 46/110 patients had DPN according to the Toronto consensus. The continuous output displayed high sensitivity and specificity for DB-HRV (91%, 83%), CNFD (88%, 78%), and SNAP (88%, 83%), whereas the categorical output showed high sensitivity but low specificity. The optimal cut-off points were 90% for the detection of autonomic dysfunction (DB-HRV) and 80% for small fibre neuropathy (CNFD). The diagnostic efficacy of the continuous Neuropad output for abnormal DB-HRV (AUC: 91%, P = 0.0003) and CNFD (AUC: 82%, P = 0.01) was better than for PMNCV (AUC: 60%). The categorical output showed no significant difference in diagnostic efficacy for these same measures. An image analysis algorithm generating a continuous output (Sudometrics) improved the diagnostic ability of Neuropad, particularly in detecting autonomic and small fibre neuropathy.

OBJECTIVE:Quantitative assessment of small fiber damage is key to the early diagnosis and assessment of progression or regression of diabetic sensorimotor polyneuropathy (DSPN). Intraepidermal nerve fiber density (IENFD) is the current gold standard, but corneal confocal microscopy (CCM), an in vivo ophthalmic imaging modality, has the potential to be a noninvasive and objective image biomarker for identifying small fiber damage. The purpose of this study was to determine the diagnostic performance of CCM and IENFD by using the current guidelines as the reference standard.RESEARCH DESIGN AND METHODS:Eighty-nine subjects (26 control subjects and 63 patients with type 1 diabetes), with and without DSPN, underwent a detailed assessment of neuropathy, including CCM and skin biopsy.RESULTS:Manual and automated corneal nerve fiber density (CNFD) (P < 0.0001), branch density (CNBD) (P < 0.0001) and length (CNFL) (P < 0.0001), and IENFD (P < 0.001) were significantly reduced in patients with diabetes with DSPN compared with control subjects. The area under the receiver operating characteristic curve for identifying DSPN was 0.82 for manual CNFD, 0.80 for automated CNFD, and 0.66 for IENFD, which did not differ significantly (P = 0.14).CONCLUSIONS:This study shows comparable diagnostic efficiency between CCM and IENFD, providing further support for the clinical utility of CCM as a surrogate end point for DSPN.

PURPOSE:To assess the diagnostic validity of a fully automated image analysis algorithm of in vivo confocal microscopy images in quantifying corneal subbasal nerves to diagnose diabetic neuropathy.METHODS:One hundred eighty-six patients with type 1 and type 2 diabetes mellitus (T1/T2DM) and 55 age-matched controls underwent assessment of neuropathy and bilateral in vivo corneal confocal microscopy (IVCCM). Corneal nerve fiber density (CNFD), branch density (CNBD), and length (CNFL) were quantified with expert, manual, and fully-automated analysis. The areas under the curve (AUC), odds ratios (OR), and optimal thresholds to rule out neuropathy were estimated for both analysis methods.RESULTS:Neuropathy was detected in 53% of patients with diabetes. A significant reduction in manual and automated CNBD (P < 0.001) and CNFD (P < 0.0001), and CNFL (P < 0.0001) occurred with increasing neuropathic severity. Manual and automated analysis methods were highly correlated for CNFD (r = 0.9, P < 0.0001), CNFL (r = 0.89, P < 0.0001), and CNBD (r = 0.75, P < 0.0001). Manual CNFD and automated CNFL were associated with the highest AUC, sensitivity/specificity and OR to rule out neuropathy.CONCLUSIONS:Diabetic peripheral neuropathy is associated with significant corneal nerve loss detected with IVCCM. Fully automated corneal nerve quantification provides an objective and reproducible means to detect human diabetic neuropathy.

The automatic detection and localization of anatomical landmarks has wide application, including intra and interpatient registration, study location and navigation, and the targeting of specialized algorithms. In this paper, we demonstrate the automatic detection and localization of 127 anatomically defined landmarks distributed throughout the body, excluding arms. Landmarks are defined on the skeleton, vasculature and major organs. Our approach builds on the classification forests method,1 using this classifier with simple image features which can be efficiently computed. For the training and validation of the method we have used 369 CT volumes on which radiographers and anatomists have marked ground truth (GT) - that is the locations of all defined landmarks occurring in that volume. A particular challenge is to deal with the wide diversity of datasets encountered in radiology practice. These include data from all major scanner manufacturers, different extents covering single and multiple body compartments, truncated cardiac acquisitions, with and without contrast. Cases with stents and catheters are also represented. Validation is by a leave-one-out method, which we show can be efficiently implemented in the context of decision forest methods. Mean location accuracy of detected landmarks is 13.45mm overall; execution time averages 7s per volume on a modern server machine. We also present localization ROC analysis to characterize detection accuracy - that is to decide if a landmark is or is not present in a given dataset.

Purpose: To establish intraobserver and interobserver repeatability, agreement, and symmetry of corneal nerve fiber (NF) morphology in healthy subjects using in vivo corneal confocal microscopy.Methods: Nineteen subjects underwent in vivo corneal confocal microscopy (Heidelberg Retinal Tomograph III Rostock Cornea Module) at baseline and 7 days apart. Bland-Altman plots were generated to assess agreement, and the intraclass correlation coefficient and coefficient of repeatability were calculated to estimate intraobserver and interobserver repeatability for corneal NF density (numbers per square millimeter), nerve branch density (NBD; numbers per square millimeter), NF length (millimeters per square millimeter), and NF tortuosity coefficient. Symmetry between the right and left eyes was also assessed.Results: Intraclass correlation coefficient and coefficient of repeatability for intraobserver repeatability were 0.66 to 0.74 and 0.17 to 0.64, for interobserver repeatability 0.54 to 0.93 and 0.15 to 0.85, and for symmetry 0.34 to 0.77 and 0.17 to 0.63, respectively. NBD demonstrated low repeatability.Conclusions: This study demonstrates good repeatability for the manual assessment of all major corneal NF parameters with the exception of NBD, which highlights the difficulty in defining nerve branches and suggests the need for experienced observers or automated image analysis to ensure optimal repeatability.

Diabetic peripheral neuropathy (DPN) is one of the most common long-term complications of diabetes. The accurate detection and quantification of DPN are important for defining at-risk patients, anticipating deterioration, and assessing new therapies. Current methods of detecting and quantifying DPN, such as neurophysiology, lack sensitivity, require expert assessment and focus primarily on large nerve fibers. However, the earliest damage to nerve fibers in diabetic neuropathy is to the small nerve fibers. At present, small nerve fiber damage is currently assessed using skin/nerve biopsy; both are invasive technique and are not suitable for repeated investigations.

Diabetic peripheral neuropathy (DPN) is one of the most common long term complications of diabetes. Corneal confocal microscopy (CCM) image analysis is a novel non-invasive technique which quantifies corneal nerve fibre damage and enables diagnosis of DPN. This paper presents an automatic analysis and classification system for detecting nerve fibres in CCM images based on a multi-scale adaptive dual-model detection algorithm. The algorithm exploits the curvilinear structure of the nerve fibres and adapts itself to the local image information. Detected nerve fibres are then quantified and used as feature vectors for classification using random forest (RF) and neural networks (NNT) classifiers. We show, in a comparative study with other well known curvilinear detectors, that the best performance is achieved by the multi-scale dual model in conjunction with the NNT classifier. An evaluation of clinical effectiveness shows that the performance of the automated system matches that of ground-truth defined by expert manual annotation.

Corneal Confocal Microscopy (CCM) imaging is a non-invasive surrogate of detecting, quantifying and monitoring diabetic peripheral neuropathy. This paper presents an automated method for detecting nerve-fibres from CCM images using a dual-model detection algorithm and compares the performance to well-established texture and feature detection methods. The algorithm comprises two separate models, one for the background and another for the foreground (nerve-fibres), which work interactively. Our evaluation shows significant improvement (p ≈ 0) in both error rate and signal-to-noise ratio of this model over the competitor methods. The automatic method is also evaluated in comparison with manual ground truth analysis in assessing diabetic neuropathy on the basis of nerve-fibre length, and shows a strong correlation (r = 0.92). Both analyses significantly separate diabetic patients from control subjects (p ≈ 0).