Multi-Channel Neural Network for Assessing Neonatal Pain from Videos
Department of Computer Science and Engineering, University of South Florida（南佛罗里达大学计算机科学与工程系）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；College of Medicine Pediatrics, USF Health, University of South Florida（南佛罗里达大学USF健康医学院儿科学院）；The current affiliation of Dr. Zamzmi is the National Library of Medicine, National Institutes of Health, MD, USA（（此处为Dr. Zamzmi的当前所属机构，与上文重复，按要求保留英文及中文翻译）美国国立卫生研究院国家医学图书馆）
Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing. The current clinical standard for assessing neonatal pain is intermittent and highly subjective. This discontinuity and subjectivity can lead to inconsistent assessment, and therefore, inadequate treatment. In this paper, we propose a multi-channel deep learning framework for assessing neonatal pain from videos. The proposed framework integrates information from two pain indicators or channels, namely facial expression and body movement, using convolutional neural network (CNN). It also integrates temporal information using a recurrent neural network (LSTM). The experimental results prove the efficiency and superiority of the proposed temporal and multi-channel framework as compared to existing similar methods.
University of South Florida（南佛罗里达大学）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；USF Health（南佛罗里达大学健康学院）
Neonatal pain assessment in clinical environments is challenging as it is discontinuous and biased. Facial/body occlusion can occur in such settings due to clinical condition, developmental delays, prone position, or other external factors. In such cases, crying sound can be used to effectively assess neonatal pain. In this paper, we investigate the use of a novel CNN architecture (N-CNN) along with other CNN architectures (VGG16 and ResNet50) for assessing pain from crying sounds of neonates. The experimental results demonstrate that using our novel N-CNN for assessing pain from the sounds of…
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（牛津大学工程科学系生物医学工程研究所）；Department of Paediatrics, University of Oxford（牛津大学儿科学系）；Universidad San Francisco de Quito USFQ, Colegio de Ciencias Biologicas y Ambientales（基多圣弗朗西斯科大学生物与环境科学学院）
Transfer learning using pre-trained Convolutional Neural Networks (CNNs) has been successfully applied to images for different classification tasks. In this paper, we propose a new pipeline for pain expression recognition in neonates using transfer learning. Specifically, we propose to exploit a pre-trained CNN that was originally trained on a relatively similar dataset for face recognition (VGG Face) as well as CNNs that were pre-trained on a relatively different dataset for image classification (iVGG F,M, and S) to extract deep features from neonates' faces. In the final stage, several super…
This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain. It specifically investigates the use of Bilinear Convolutional Neural Network (B-CNN) to extract facial features during different levels of postoperative pain followed by modeling the temporal pattern using Recurrent Neural Network (RNN). Although acute and postoperative pain have some common characteristics (e.g., visual action units), postoperative pain has a different dynamic, and it evolves in a unique pattern over time. Our experimental results ind…
University of South Florida（南佛罗里达大学）；USF Health（南佛罗里达大学健康科学中心）
The current practice for assessing neonatal postoperative pain relies on bedside caregivers. This practice is subjective, inconsistent, slow, and discontinuous. To develop a reliable medical interpretation, several automated approaches have been proposed to enhance the current practice. These approaches are unimodal and focus mainly on assessing neonatal procedural (acute) pain. As pain is a multimodal emotion that is often expressed through multiple modalities, the multimodal assessment of pain is necessary especially in case of postoperative (acute prolonged) pain. Additionally, spatio-tempo…
InfantFace: Detecting infant faces in neonatal clinical environments
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（牛津大学生物医学工程研究所、工程科学系）；Department of Paediatrics, University of Oxford（牛津大学儿科系）；Universidad San Francisco de Quito USFQ, Colegio de Ciencias Biologicas y Ambientales（奎托大学圣弗朗西斯科德奎托大学，生物科学与环境学院）
PainSeeker: An Automated Method for Assessing Pain in Rats Through Facial Expressions
Affiliated Stomatological Hospital, Nanjing Medical University（南京医科大学附属口腔医院）；School of Biological Science and Medical Engineering, Southeast University（东南大学生物科学与医学工程学院）
Neonatal Face and Facial Landmark Detection from Video Recordings
Monash University（莫纳什大学）；University British Columbia（不列颠哥伦比亚大学）；Monash Newborn（莫纳什新生儿中心）；Monash Institute of Medical Engineering（莫纳什医学工程研究所）；Monash Data Future Institute（莫纳什数据未来研究所）
Northwest University（西北大学）；University of Oulu（奥卢大学）；Northwestern Polytechnical University（西北工业大学）
Automatic pain recognition is paramount for medical diagnosis and treatment. The existing works fall into three categories: assessing facial appearance changes, exploiting physiological cues, or fusing them in a multi-modal manner. However, (1) appearance changes are easily affected by subjective factors which impedes objective pain recognition. Besides, the appearance-based approaches ignore long-range spatial-temporal dependencies that are important for modeling expressions over time; (2) the physiological cues are obtained by attaching sensors on human body, which is inconvenient and uncomf…
Sheep Facial Pain Assessment Under Weighted Graph Neural Networks
CISTER Research Center, Porto, Portugal（CISTER研究中心，葡萄牙 Porto）；Faculty of Engineering University of Porto, Portugal（葡萄牙 Porto大学工程学院）；Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000, Lille, France（法国 Lille 大学，CNRS，Centrale Lille，UMR 9189 CRIStAL）；IMT Nord Europe, Institut Mines-Télécom, Univ. Lille, Centre for Digital Systems, F-59000 , Lille, France（法国 IMT Nord Europe，Institut Mines-Télécom，Univ. Lille，数字系统中心）
FX Palo Alto Laboratory（FX帕洛阿尔托实验室）；KK Women’s and Children’s Hospital（KK妇女儿童医院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）
Managing post-surgical pain is critical for successful surgical outcomes. One of the challenges of pain management is accurately assessing the pain level of patients. Self-reported numeric pain ratings are limited because they are subjective, can be affected by mood, and can influence the patient's perception of pain when making comparisons. In this paper, we introduce an approach that analyzes 2D and 3D facial keypoints of post-surgical patients to estimate their pain intensity level. Our approach leverages the previously unexplored capabilities of a smartphone to capture a dense 3D represent…
Quantitative Movement Testing: Measuring Chronic Pain Patient Movements from a Single Smartphone Video
Nuffield Department of Clinical Neurosciences, University of Oxford（临床神经科学系,Nuffield大学,牛津大学）；Max Planck Institute of Biological Cybernetics（生物信息学研究所）；Oxford Gait Laboratory, University of Oxford（牛津大学步态实验室）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Institute of Biomedical Engineering, University of Oxford（生物医学工程研究所,牛津大学）；Mayo Clinic（梅奥诊所）
