Evidence map›Paper›PMID 40822979›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2025

DEEP AUTOMATIC ALIGNMENT OF MPOX DERMATOLOGICAL HAND PHOTOGRAPHY.

Bohan Jiang, Andrew McNeil, Yihao Liu, Gaurav Rudravaram, Inga Saknite, Placide Mbala-Kingebeni, Olivier Tshiani Mbaya, Tyra Silaphet, Rachel Weiss, Lori E Dodd and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Bohan JiangDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Andrew McNeilDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Yihao LiuDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.
Gaurav RudravaramDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Inga SakniteDepartment of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA.
Placide Mbala-KingebeniInstitut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo.
Olivier Tshiani MbayaInstitut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo.
Tyra SilaphetDepartment of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA.
Rachel WeissDepartment of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA.
Lori E DoddClinical Trials Research Section, Division of Clinical Research, National Institute of Allergy and Infectious Disease, Bethesda, MD, USA.
Veronique NussenblattLaboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA.
Daniel MoyerDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.
Bennett A LandmanDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Benoit M DawantDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Eric R TkaczykDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.

Funding

WORK ORDER 126643 B539 EXPAND IC SUITE75N91019D00024 · NIAID · LEIDOS BIOMEDICAL RESEARCH, INC. · PI BRISCOE, LYNN · 2019 to 2025
$3932.6M
NRSA Training CoreTL1TR002244 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Julie A. Bastarache · 2017 to 2026
$4.6M
Artificial intelligence to estimate extent of cGVHD from patient photosR01HL169944 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Eric R Tkaczyk · 2024 to 2026
$2.3M
CSRD VA IK2 CX001785NCATS NIH HHS TL1 TR002244NCI NIH HHS 75N91019D00024NHLBI NIH HHS R01 HL169944
6 · The paper itself

Abstract

Mpox is a viral illness with heavy cutaneous involvement. Automatic tracking of mpox lesion progression is critical in determining the resolution of evolving lesions. This work introduces a novel application of deep learning for lesion monitoring through alignment of dermatological hand photographs. By adapting the VoxelMorph framework for 2D photographic data, we explore key point alignment across serial images. We trained our neural network model on a unique dataset of 1,658 hand images and evaluated its performance on a test set of 254 images. Additionally, we validated the method's generalizability with a supplementary set of 500 images, which included extensive Mpox infection. Our findings indicate modest yet significant improvements in key points and lesion center registration across different regularization strengths. Although promising, the complexity of hand structure presents challenges, requiring cautious application and further refinement, especially in regions with intense spatial discontinuities, such as interdigital areas.

Indexed as

Deep LearningDermatological PhotographyImage RegistrationMpoxVoxelMorph

Identifiers

PMID40822979
PMCPMC12352120

What OpenQuestion holds

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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.