Contactless monitoring of respiratory rate (RR) and heart rate (HR) in non-acuity settings: a clinical validity study.

OBJECTIVE: Patient monitoring in general wards primarily involves intermittent observation of temperature, heart rate (HR), respiratory rate (RR) and blood pressure performed by the nursing staff. Several hours can lapse between such measurements, and the patient may go unobserved. Despite the growing widespread use of sensors to monitor vital signs and physical activities of healthy individuals, most acutely ill hospitalised patients remain unmonitored, leaving them at an increased risk. We investigated whether a contactless monitoring system could measure vital parameters, such as HR and RR, in a real-world hospital setting. DESIGN: A cross-sectional prospective study. SETTING AND PARTICIPANTS: We examined the suitability of employing a non-contact monitoring system in a low-acuity setup at a tertiary care hospital in India. Measurements were performed on 158 subjects, with data acquired through contactless monitoring from the general ward and dialysis unit. OUTCOME MEASURES: Vital parameters (RR and HR) were measured using a video camera in a non-acuity setting. RESULTS: Three distinct combinations of contactless monitoring afforded excellent accuracy. Contactless RR monitoring was linearly correlated with Alice NightOne and manual counts, presenting coefficients of determination of 0.88 and 0.90, respectively. Contactless HR monitoring presented a coefficient of determination of 0.91. The mean absolute errors were 0.84 and 2.15 beats per minute for RR and HR, respectively. CONCLUSIONS: Compared with existing Food and Drug Administration-approved monitors, the findings of the present study revealed that contactless monitoring of RR and HR accurately represented study populations in non-acuity settings. Contactless video monitoring is an unobtrusive and dependable method for monitoring and recording RR and HR. Further research is needed to validate its dependability and utility in other settings, including acute care. TRIAL REGISTRATION NUMBER: CTRI/2018/11/016246.

A multi-institution evaluation of deformable image registration algorithms for automatic organ delineation in adaptive head and neck radiotherapy.

BACKGROUND: Adaptive Radiotherapy aims to identify anatomical deviations during a radiotherapy course and modify the treatment plan to maintain treatment objectives. This requires regions of interest (ROIs) to be defined using the most recent imaging data. This study investigates the clinical utility of using deformable image registration (DIR) to automatically propagate ROIs. METHODS: Target (GTV) and organ-at-risk (OAR) ROIs were non-rigidly propagated from a planning CT scan to a per-treatment CT scan for 22 patients. Propagated ROIs were quantitatively compared with expert physician-drawn ROIs on the per-treatment scan using Dice scores and mean slicewise Hausdorff distances, and center of mass distances for GTVs. The propagated ROIs were qualitatively examined by experts and scored based on their clinical utility. RESULTS: Good agreement between the DIR-propagated ROIs and expert-drawn ROIs was observed based on the metrics used. 94% of all ROIs generated using DIR were scored as being clinically useful, requiring minimal or no edits. However, 27% (12/44) of the GTVs required major edits. CONCLUSION: DIR was successfully used on 22 patients to propagate target and OAR structures for ART with good anatomical agreement for OARs. It is recommended that propagated target structures be thoroughly reviewed by the treating physician.

Aortic thrombus segmentation using narrow band active contour model.

This paper proposes 2D active contour approach for segmenting thrombus volume from 3D CT images of abdominal aortic aneurysm (AAA). The major challenges in segmenting thrombus are in part because of lack of delineating contrast at anatomical boundaries due to overlap of other soft tissues and artifacts arising from stents and calcium deposits. In the present approach first the bone structures are removed from the image so that these nearby high intensity regions do not interfere in the segmentation process. Next morphological operation is done on the bone-removed image to reduce the effect of streak artifacts. The order of these two operations can be inter-changed. Then, a manual contour is initialized on an axial slice of the pre-processed image and deformed and subsequently propagated to the consecutive slices for deformation. The snake process is governed by force field defined by intensity-based object-ness measure within a band defined by local image properties. The proposed algorithm has been tested on 7 CT images and compared with the ground truth obtained from manual segmentation by radiologist and accuracy between the range 93.16% to 85.08% is observed.