Activity Recognition From Newborn Resuscitation Videos

• URL: http://arxiv.org/abs/2303.07789v1
• Date: Tue, 14 Mar 2023 11:04:32 GMT
• Title: Activity Recognition From Newborn Resuscitation Videos
• Authors: {\O}yvind Meinich-Bache, Simon Lennart Austnes, Kjersti Engan, Ivar Austvoll, Trygve Eftest{\o}l, Helge Myklebust, Simeon Kusulla, Hussein Kidanto and Hege Ersdal
• Abstract summary: A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania. We propose a two-step deep neural network system, ORAA-net, to do activity recognition during newborn resuscitation. The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67 %, a mean recall of 77,64 %, and a mean accuracy of 92.40 %.
• Score: 3.54339629317433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: Birth asphyxia is one of the leading causes of neonatal deaths. A key for survival is performing immediate and continuous quality newborn resuscitation. A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania, and the aim is to analyze the treatment and its effect on the newborn outcome. An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes. Methods: We propose a two-step deep neural network system, ORAA-net, utilizing low-quality video recordings of resuscitation episodes to do activity recognition during newborn resuscitation. The first step is to detect and track relevant objects using Convolutional Neural Networks (CNN) and post-processing, and the second step is to analyze the proposed activity regions from step 1 to do activity recognition using 3D CNNs. Results: The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67 %, a mean recall of 77,64 %, and a mean accuracy of 92.40 %. Moreover, the accuracy of the estimated number of Health Care Providers (HCPs) present during the resuscitation episodes was 68.32 %. Conclusion: The results indicate that the proposed CNN-based two-step ORAAnet could be used for object detection and activity recognition in noisy low-quality newborn resuscitation videos. Significance: A thorough analysis of the effect the different resuscitation activities have on the newborn outcome could potentially allow us to optimize treatment guidelines, training, debriefing, and local quality improvement in newborn resuscitation.

Related papers

• Advancing Newborn Care: Precise Birth Time Detection Using AI-Driven Thermal Imaging with Adaptive Normalization [1.101731711817642]
  We investigate the fusion of Artificial Intelligence (AI) and thermal imaging to develop the first AI-driven Time of Birth detector. Our methodology involves a three-step process: first, we propose an adaptive normalization method based on Gaussian mixture models (GMM) to mitigate issues related to temperature variations. A precision of 88.1% and a recall of 89.3% are reported in the detection of the newborn within thermal frames during performance evaluation.
  arXiv  Detail & Related papers  (2024-10-14T13:20:51Z)
• Dynamic Gaussian Splatting from Markerless Motion Capture can Reconstruct Infants Movements [2.44755919161855]
  This work paves the way for advanced movement analysis tools that can be applied to diverse clinical populations. We explored the application of dynamic Gaussian splatting to sparse markerless motion capture data. Our results demonstrate the potential of this method in rendering novel views of scenes and tracking infant movements.
  arXiv  Detail & Related papers  (2023-10-30T11:09:39Z)
• Automatic Infant Respiration Estimation from Video: A Deep Flow-based Algorithm and a Novel Public Benchmark [10.097634735211654]
  We develop a deep-learning method for estimating respiratory rate and waveform from plain video footage in natural settings. Our model supports the first public annotated infant respiration dataset with 125 videos. When trained and tested on the AIR-125 infant data, our method significantly outperforms other state-of-the-art methods in respiratory rate estimation.
  arXiv  Detail & Related papers  (2023-07-24T19:59:15Z)
• Object Detection During Newborn Resuscitation Activities [3.5661795505491445]
  We propose a two-step process in order to detect activities possibly overlapping in time. The first step is to detect and track the relevant objects, like bag-mask resuscitator, heart rate sensors etc. The performance of the object detection during activities were 96.97 % (ventilations), 100 % (attaching/removing heart rate sensor) and 75 % (suction) on a test set of 20 videos.
  arXiv  Detail & Related papers  (2023-03-14T11:04:50Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• One-shot action recognition towards novel assistive therapies [63.23654147345168]
  This work is motivated by the automated analysis of medical therapies that involve action imitation games. The presented approach incorporates a pre-processing step that standardizes heterogeneous motion data conditions. We evaluate the approach on a real use-case of automated video analysis for therapy support with autistic people.
  arXiv  Detail & Related papers  (2021-02-17T19:41:37Z)
• Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs [49.32653090178743]
  convolutional neural networks (CNNs) are studied for lesion activity segmentation from two time points. CNNs are designed and evaluated that combine the information from two points in different ways. It is demonstrated that deep learning-based methods outperform classic approaches.
  arXiv  Detail & Related papers  (2020-08-05T08:49:20Z)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)
• Preterm infants' pose estimation with spatio-temporal features [7.054093620465401]
  This paper introduces the use of preterm-temporal features for limb detection and tracking. It is the first study to use depth videos acquired in the actual clinical practice for limb-pose estimation.
  arXiv  Detail & Related papers  (2020-05-08T09:51:22Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.