An AI-based Multimodal Approach to Predict Pain in Postnatal Care Scenarios

基于人工智能的多模式方法来预测产后护理场景中的疼痛

• 批准号: 10546650
• 负责人: PETER Randolph MOUTON
• 金额: $ 31.52万
• 依托单位: STEREOLOGY RESOURCE CENTER, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-10 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546650
• 关键词: Absence of pain sensation; Acetaminophen; Address; Admission activity; Adverse effects; Anatomy; Anesthesia procedures; Animals; Behavior; Biological Markers; Biological Sciences; Blood; Caregivers; Clinical; Computer Vision Systems; Computers; Crying; Data; Data Scientist; Detection; Educational workshop; Facial Expression; Fentanyl; Frequencies; Future; General Hospitals; Hair; Health; Health Benefit; Hour; Human; Hydrocortisone; Ibuprofen; In Vitro; Incidence; Industry; Innovation Corps; Intention; Intervention; Interview; Intravenous; Ketorolac; Label; Length of Stay; Life; Machine Learning; Medical; Methadone; Modeling; Monitor; Morphine; Movement; Narcotics; Neonatal; Neonatal Abstinence Syndrome; Neonatal Intensive Care Units; Neurologic; Newborn Infant; Norepinephrine; Operative Surgical Procedures; Opiate Addiction; Opioid; Opioid Analgesics; Pain; Pain Measurement; Pain management; Patients; Performance; Personal Satisfaction; Pharmaceutical Preparations; Phase; Physiology; Postnatal Care; Postoperative Pain; Postoperative Period; Probability; Procedures; Public Health; Regimen; Research; Savings; Sedation procedure; Signal Transduction; Skin; Specialist; Stress; Survival Rate; System; Techniques; Technology; Testing; Time; Training; Trauma; Triage; Urine; Videotape; Withdrawal; Work; base; cohort; convolutional neural network; cost; deep neural network; detection platform; innovative technologies; machine learning algorithm; meter; multimodal data; multimodality; neonatal brain; neonate; non-invasive system; non-opioid analgesic; novel; opioid sparing; opioid use; opioid withdrawal; pain scale; patient population; phase 2 study; side effect

PROJECT SUMMARY Advances in technology and surgical procedures in the past decade have led to a remarkable increase in numbers of newborns subjected to lifesaving surgery. These postoperative neonates are customarily triaged to neonatal intensive care units (NICUs) for pain management with opioids, primarily morphine, fentanyl, and methadone. However, substantial evidence from in- vitro, animal, and human studies strongly suggests this severe pain-to-opioids regimen causes long-lasting and likely permanent traumatic harm to the developing neurological systems of neonates. We propose a novel machine learning and computer vision approach for early pain detection (EPD) with emphasis on postoperative neonates in NICU. By alerting NICU caregivers a minimum of ~ 30 minutes prior to pain onset, EPD will allow NICU staff to use fast-acting, opioid-sparing medications, e.g., intravenous paracetamol, ibuprofen, or ketorolac, in conjunction with non- pharmacological approaches, to “stay ahead of the pain” while avoiding opioid treatments, tolerance, withdrawal, and their associated side effects. Our team of NICU specialists, bioscientists, and computer experts will demonstrate proof-of- concept for reliable EPD in post-surgical neonates using the following aims: 1) Collect clinical information and multimodal data (facial expression, body movement, crying frequency, vital signs) for post-surgical pain prediction in neonates. We will collect and label multi-modal signals (facial expression, body movements, crying frequency, vital signs) from ~ 60 neonates in post-surgical pain at the NICU at Tampa General Hospital (TGH). We will combine these data with similar data from another cohort of ~60 neonates (total ~ 120 neonates) collected using the same system and approach at TGH from 2019 to 2022. 2) Show proof-of-concept for predicting the onset of post-surgical pain in neonates. The multimodal data collected from the training cases in Aim 1 will provide the ground truth for training a convolutional neural network to predict time-to-onset of pain for postoperative neonates in the testing cases. The performance target for the EPD in the test cases is pain prediction ~ 30 minutes prior to pain onset with a 90% confidence probability. Our intention is to disrupt the current standard [surgery; sedation; postoperative pain; opioid dependence, tolerance, withdrawal; discharge] in favor of a safer opioid-sparing approach [surgery; sedation; non-opioid treatment; discharge]. Our Phase 2 studies will add data from more diverse patient populations and examine the possible effects of EPD on stress biomarkers, e.g., cortisol, norepinephrine in hair, skin, blood, or urine. The major benefit to public health will be protection of perhaps the most vulnerable patient populations from unnecessary damage to their future health and well-being.

项目总结