Background: The management of procedural pain in infants is suboptimal, in part, compounded by the scarcity of a simple, accurate, and reliable method of assessing such pain. In this study, we aimed to evaluate the psychometric properties of the PainChek Infant, a point-of-care mobile application that uses automated facial evaluation and analysis in the assessment of procedural pain in infants., Methods: Video recordings of 40 infants were randomly chosen from a purposely assembled digital library of 410 children undergoing immunisation as part of their standard care in Prishtina, Kosovo, between April 4, 2017, and July 11, 2018. For each infant recording, four 10 s video segments were extracted, corresponding to baseline, vaccine preparation, during vaccination, and recovery. Four trained assessors did pain assessments on the video segments of 30 infants, using PainChek Infant standard, PainChek Infant adaptive, the Neonatal Facial Coding System-Revised (NFCS-R) single, the NFCS-R multiple, and the Observer administered Visual Analogue Scale (ObsVAS), on two separate occasions. PainChek Infant's performance was compared to NFCS-R and ObsVAS using correlation in changes in pain scores, intra-rater and inter-rater reliability, and internal consistency., Findings: 4303 pain assessments were completed in two separate testing sessions, on Aug 31, and Oct 19, 2020. The study involved videos of 40 infants aged 2·2-6·9 months (median age 3·4 months [IQR 2·3-4·5]). All pain assessment tools showed significant changes in the recorded pain scores across the four video segments (p≤0·0006). All tools were found to be responsive to procedure-induced pain, with the degree of change in pain scores not influenced by pre-vaccination pain levels. PainChek Infant pain scores showed good correlation with NFCS-R and ObsVAS scores (r=0·82-0·88; p<0·0001). PainChek Infant also showed good to excellent inter-rater reliability (ICC=0·81-0·97, p<0·001) and high levels of internal consistency (α=0·82-0·97)., Interpretation: PainChek Infant's use of automated facial expression analysis could offer a valid and reliable means of assessing and monitoring procedural pain in infants. Its clinical utility in clinical practice requires further research., Funding: PainChek., Competing Interests: Declaration of interests KH and JDH are shareholders in PainChek (formerly known as EPAT Technologies), which is commercialising the PainChek Infant. They are also named as coinventors with Mustafa Atee on the patent entitled a pain assessment method and system (patent granted in Australia [AU2015306075B2], Japan [JP6657216B2], USA [US10398372B2], and China [CN106572820A]. Patent pending from Europe [EP3182893A4], and from the World Intellectual Property Organization [WO2016025989A1]). KH is employed as a consultant by PainChek, while also serving as an associate Professor at the University of Prishtina, Kosovo and university associate at the Curtin Medical School, Curtin University, WA, Australia. JDH is employed as the chief scientific officer of PainChek and holds an adjunct Professor appointment in the Curtin Medical School, Curtin University, WA, Australia. PTC was engaged through DATaR Consulting and was paid as an independent private consultant to undertake the biostatistical analysis for the project by PainChek. PTC also holds adjunct appointments at the Institute for Health Research, the University of Notre Dame Australia, Fremantle, WA, Australia, and School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)