Evidence map›Paper›PMID 36939418›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2022

Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation.

Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Peter R Mouton, Kanwaljeet J S Anand, Terri Ashmeade, Stephanie Prescott, Yangxin Huang, Yu Sun

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
21.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.

  1. Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026
    Pooled it
  2. Clinical rating scales for assessing pain in newborn infants.The Cochrane database of systematic reviews · 2025
    Pooled it
  3. Review
  4. Review
  5. Review
  6. Explainable AI (XAI) for Neonatal Pain Assessment via Influence Function Modification.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024
    Article
  7. Enhancing Neonatal Pain Assessment Transparency via Explanatory Training Examples Identification.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Md Sirajus SalekinUniversity of South Florida, Tampa, FL, USA.
Ghada ZamzmiUniversity of South Florida, Tampa, FL, USA.
Dmitry GoldgofUniversity of South Florida, Tampa, FL, USA.
Peter R MoutonSRC Biosciences, Tampa, FL, USA.
Kanwaljeet J S AnandStanford University, Stanford, CA, USA.
Terri AshmeadeUniversity of South Florida, Tampa, FL, USA.
Stephanie PrescottUniversity of South Florida, Tampa, FL, USA.
Yangxin HuangUniversity of South Florida, Tampa, FL, USA.
Yu SunUniversity of South Florida, Tampa, FL, USA.
University of South Florida · USStanford University · US

Funding

A Multimodal Approach for Monitoring Prolonged Acute Pain in NeonatesR21NR018756 · NINR · UNIVERSITY OF SOUTH FLORIDA · PI SUN, YU · 2020 to 2021
$401k
An AI-based Multimodal Approach to Predict Pain in Postnatal Care ScenariosR41HD109086 · NICHD · STEREOLOGY RESOURCE CENTER, INC. · PI MOUTON, PETER RANDOLPH, SUN, YU · 2022 to 2022
$315k
NICHD NIH HHS R41 HD109086NINR NIH HHS R21 NR018756
6 · The paper itself

Abstract

Artificial Intelligence (AI)-based methods allow for automatic assessment of pain intensity based on continuous monitoring and processing of subtle changes in sensory signals, including facial expression, body movements, and crying frequency. Currently, there is a large and growing need for expanding current AI-based approaches to the assessment of postoperative pain in the neonatal intensive care unit (NICU). In contrast to acute procedural pain in the clinic, the NICU has neonates emerging from postoperative sedation, usually intubated, and with variable energy reserves for manifesting forceful pain responses. Here, we present a novel multi-modal approach designed, developed, and validated for assessment of neonatal postoperative pain in the challenging NICU setting. Our approach includes a robust network capable of efficient reconstruction of missing modalities (e.g., obscured facial expression due to intubation) using an unsupervised spatio-temporal feature learning with a generative model for learning the joint features. Our approach generates the final pain score along with the intensity using an attentional cross-modal feature fusion. Using experimental dataset from postoperative neonates in the NICU, our pain assessment approach achieves superior performance (AUC 0.906, accuracy 0.820) as compared to the state-of-the-art approaches.

Indexed as

Generative modelMultimodal learningNeonatal painNICUPostoperative pain

Identifiers

PMID36939418
PMCPMC10018439
OpenAlexW4295934621

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.