Unsupervised Multi-Latent Space RL Framework for Video Summarization in Ultrasound Imaging.

The COVID-19 pandemic has highlighted the need for a tool to speed up triage in ultrasound scans and provide clinicians with fast access to relevant information. To this end, we propose a new unsupervised reinforcement learning (RL) framework with novel rewards to facilitate unsupervised learning by avoiding tedious and impractical manual labelling for summarizing ultrasound videos. The proposed framework is capable of delivering video summaries with classification labels and segmentations of key landmarks which enhances its utility as a triage tool in the emergency department (ED) and for use in telemedicine. Using an attention ensemble of encoders, the high dimensional image is projected into a low dimensional latent space in terms of: a) reduced distance with a normal or abnormal class (classifier encoder), b) following a topology of landmarks (segmentation encoder), and c) the distance or topology agnostic latent representation (autoencoders). The summarization network is implemented using a bi-directional long short term memory (Bi-LSTM) which utilizes the latent space representation from the encoder. Validation is performed on lung ultrasound (LUS), that typically represent potential use cases in telemedicine and ED triage acquired from different medical centers across geographies (India and Spain). The proposed approach trained and tested on 126 LUS videos showed high agreement with the ground truth with an average precision of over 80% and average F1 score of well over 44 ±1.7 %. The approach resulted in an average reduction in storage space of 77% which can ease bandwidth and storage requirements in telemedicine.

Unsupervised landmark detection and classification of lung infection using transporter neural networks.

Supervised deep learning techniques have been very popular in medical imaging for various tasks of classification, segmentation, and object detection. However, they require a large number of labelled data which is expensive and requires many hours of careful annotation by experts. In this paper, an unsupervised transporter neural network framework with an attention mechanism is proposed to automatically identify relevant landmarks with applications in lung ultrasound (LUS) imaging. The proposed framework identifies key points that provide a concise geometric representation highlighting regions with high structural variation in the LUS videos. In order for the landmarks to be clinically relevant, we have employed acoustic propagation physics driven feature maps and angle-controlled Radon Transformed frames at the input instead of directly employing the gray scale LUS frames. Once the landmarks are identified, the presence of these landmarks can be employed for classification of the given frame into various classes of severity of infection in lung. The proposed framework has been trained on 130 LUS videos and validated on 100 LUS videos acquired from multiple centres at Spain and India. Frames were independently assessed by experts to identify clinically relevant features such as A-lines, B-lines, and pleura in LUS videos. The key points detected showed high sensitivity of 99% in detecting the image landmarks identified by experts. Also, on employing for classification of the given lung image into normal and abnormal classes, the proposed approach, even with no prior training, achieved an average accuracy of 97% and an average F1-score of 95% respectively on the task of co-classification with 3-fold cross-validation.

The Medial and Posterior Compression of the Distal Femoral Vein: An Underused Approach to Detect Venous Thrombosis.

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Quantitative pleural line characterization outperforms traditional lung texture ultrasound features in detection of COVID-19.

BACKGROUND AND OBJECTIVE: Lung ultrasound is an inherently user-dependent modality that could benefit from quantitative image analysis. In this pilot study we evaluate the use of computer-based pleural line (p-line) ultrasound features in comparison to traditional lung texture (TLT) features to test the hypothesis that p-line thickening and irregularity are highly suggestive of coronavirus disease 2019 (COVID-19) and can be used to improve the disease diagnosis on lung ultrasound. METHODS: Twenty lung ultrasound images, including normal and COVID-19 cases, were used for quantitative analysis. P-lines were detected by a semiautomated segmentation method. Seven quantitative features describing thickness, margin morphology, and echo intensity were extracted. TLT lines were outlined, and texture features based on run-length and gray-level co-occurrence matrix were extracted. The diagnostic performance of the 2 feature sets was measured and compared using receiver operating characteristics curve analysis. Observer agreements were evaluated by measuring interclass correlation coefficients (ICC) for each feature. RESULTS: Six of 7 p-line features showed a significant difference between normal and COVID-19 cases. Thickness of p-lines was larger in COVID-19 cases (6.27 ± 1.45 mm) compared to normal (1.00 ± 0.19 mm), P < 0.001. Among features describing p-line margin morphology, projected intensity deviation showed the largest difference between COVID-19 cases (4.08 ± 0.32) and normal (0.43 ± 0.06), P < 0.001. From the TLT line features, only 2 features, gray-level non-uniformity and run-length non-uniformity, showed a significant difference between normal cases (0.32 ± 0.06, 0.59 ± 0.06) and COVID-19 (0.22 ± 0.02, 0.39 ± 0.05), P = 0.04, respectively. All features together for p-line showed perfect sensitivity and specificity of 100; whereas, TLT features had a sensitivity of 90 and specificity of 70. Observer agreement for p-lines (ICC = 0.65-0.85) was higher than for TLT features (ICC = 0.42-0.72). CONCLUSION: P-line features characterize COVID-19 changes with high accuracy and outperform TLT features. Quantitative p-line features are promising diagnostic tools in the interpretation of lung ultrasound images in the context of COVID-19.

Eficacia de una vía de alta resolución en la evaluación del cólico renoureteral no complicado en un servicio de urgencias hospitalario: un ensayo clínico aleatorizado (Estudio STONE) / Efficacy of a fast-track pathway for managing uncomplicated renal orureteral colic in a hospital emergency department: the STONE randomized clinical trial of Sonography and Testing of a Nephrolithiasis Episode

OBJETIVO: Evaluar una vía de alta resolución (vía POC) que utiliza análisis en el punto de atención (point-of-care testing-POCT-) y ecografía en el punto de atención (point-of-care ultrasonography -POCUS-) en la sospecha del cólico renoureteral (CRU) no complicado y compararla con la vía estándar (vía STD). MÉTODO: Ensayo clínico aleatorizado, controlado, no ciego, realizado en un servicio de urgencias hospitalario (SUH). Incluyó pacientes con sospecha clínica de CRU agudo y se aleatorizaron 1:1 a seguir vía POC o vía STD. Se analizó el tiempo de estancia en el SUH, el tratamiento administrado, la proporción de diagnósticos alternativos a CRU y las complicaciones a 30 días. RESULTADOS: Entre noviembre de 2018 y octubre de 2019, se reclutaron 140 pacientes de los que se analizaron 124.El tiempo de estancia total en el SUH de la vía POC fue de 112 minutos (DE 45) y en la vía STD 244 minutos (DE102) (p < 0,001). No hubo diferencias en el tratamiento administrado en urgencias, en el número de diagnósticos alternativos, ni en las complicaciones a 30 días. CONCLUSIONES: La utilización de una vía de alta resolución del manejo del CRU en un SUH es eficaz, segura y reduce el tiempo de estancia en urgencias

OBJECTIVES: To evaluate a fast-track pathway utilizing point-of-care (POC) testing and sonography as soon as uncomplicated renal or ureteral colic is suspected and to compare the POC clinical pathway to a standard one. METHODS: Unblinded randomized controlled clinical trial in a hospital emergency department (ED). We enrolled patients with suspected uncomplicated renal or ureteral colic and randomized them to a POC or standard pathway(1:1 ratio). Duration of ED stay, treatments, the proportion of diagnoses other than uncomplicated colic, and 30-daycomplications were analyzed. RESULTS: One hundred forty patients were recruited between November 2018 and October 2019; data for 124 were analyzed. The mean (SD) total time in the ED was 112 (45) minutes in the POC arm and 244 (102) in the standard arm (P< .001). Treatments, alternative diagnoses, and complication rates did not differ. CONCLUSION: The use of a fast-track POC pathway to manage uncomplicated colic in the ED is effective and safe. It also reduces the amount of time spent in the ED

Utilidad de la ecografía a pie de cama en el body packer / Usefulness of bedside ultrasound in body packer syndrome

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