Telemedicine for international travelers through a Smartphone-based monitoring platform (Trip Doctor®).

BACKGROUND: Overall, more than 50% of international travelers develop symptoms while traveling and 55% of them seek medical assistance during the trip. We conducted a study to evaluate the usefulness of a Smartphone app called TRIP Doctor® to provide telemedicine to international travelers. METHODS: Participants over 18 years old attending our travel clinic at Hospital Clinic in Barcelona were invited to participate during 2017-2019. After downloading the app, the health status of the traveler was monitored on a daily basis, providing specific medical advice and offering remote contact with specialized physicians through an integrated chat, if needed. RESULTS: From 449 users, 59 (13%) contacted for medical assistance through the app during the trip. Main reasons for telemedicine were diarrhea (25.7%), skin conditions (19.7%) and fever (12.1%). Among patients who contacted, 90% of the travelers did not require to be referred to a local doctor. Symptomatic treatment was the main treatment prescribed (38%). In a 14.7% of the cases a follow-up was not required, a 63.2% recovered and 22.1% were loss of follow-up. After a multivariate analysis, duration of trip >14 days was found to be the only factor associated with the use of telemedicine (OR 2.2, CI 95% 1.1-4.5, p = 0.03). CONCLUSION: In conclusion, travelers using telemedicine travelled for longer periods of time and mostly contacted for mild symptoms which could be solved successfully by remote assistance with our specialized doctors.

Adaptive compounding speckle-noise-reduction filter for optical coherence tomography images.

SIGNIFICANCE: Speckle noise limits the diagnostic capabilities of optical coherence tomography (OCT) images, causing both a reduction in contrast and a less accurate assessment of the microstructural morphology of the tissue. AIM: We present a speckle-noise reduction method for OCT volumes that exploits the advantages of adaptive-noise wavelet thresholding with a wavelet compounding method applied to several frames acquired from consecutive positions. The method takes advantage of the wavelet representation of the speckle statistics, calculated properly from a homogeneous sample or a region of the noisy volume. APPROACH: The proposed method was first compared quantitatively with different state-of-the-art approaches by being applied to three different clinical dermatological OCT volumes with three different OCT settings. The method was also applied to a public retinal spectral-domain OCT dataset to demonstrate its applicability to different imaging modalities. RESULTS: The results based on four different metrics demonstrate that the proposed method achieved the best performance among the tested techniques in suppressing noise and preserving structural information. CONCLUSIONS: The proposed OCT denoising technique has the potential to adapt to different image OCT settings and noise environments and to improve image quality prior to clinical diagnosis based on visual assessment.

Automatic estimation of aortic and mitral valve displacements in dynamic CTA with 4D graph-cuts.

The location of the mitral and aortic valves in dynamic cardiac imaging is useful for extracting functional derived parameters such as ejection fraction, valve excursions, and global longitudinal strain, and when performing anatomical structures tracking using slice following or valve intervention's planning. Completely automatic segmentation methods are still challenging tasks because of their fast movements and the different positions that prevent good visibility of the leaflets along the full cardiac cycle. In this article, we propose a processing pipeline to track the displacement of the aortic and mitral valve annuli from high-resolution cardiac four-dimensional computed tomographic angiography (4D-CTA). The proposed method is based on the dynamic separation of left ventricle, left atrium and aorta using statistical shape modeling and an energy minimization algorithm based on graph-cuts and has been evaluated on a set of 15 electrocardiography-gated 4D-CTAs. We report a mean agreement distance between manual annotations and our proposed method of 2.52±1.06 mm for the mitral annulus and 2.00±0.69 mm for the aortic valve annulus based on valve locations detected from manual anatomical landmarks. In addition, we show the effect of detecting the valvular planes on derived functional parameters (ejection fraction, global longitudinal strain, and excursions of the mitral and aortic valves).

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning.

Glaucoma detection in color fundus images is a challenging task that requires expertise and years of practice. In this study we exploited the application of different Convolutional Neural Networks (CNN) schemes to show the influence in the performance of relevant factors like the data set size, the architecture and the use of transfer learning vs newly defined architectures. We also compared the performance of the CNN based system with respect to human evaluators and explored the influence of the integration of images and data collected from the clinical history of the patients. We accomplished the best performance using a transfer learning scheme with VGG19 achieving an AUC of 0.94 with sensitivity and specificity ratios similar to the expert evaluators of the study. The experimental results using three different data sets with 2313 images indicate that this solution can be a valuable option for the design of a computer aid system for the detection of glaucoma in large-scale screening programs.