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AI-powered evaluation of dementia severity based on clinical data and visual scoring systems (MTA, ERICA, GCA) from MRI.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § Results › Dementia prediction results with visual score system › SHAP feature attribution analysis ↔ clinical/config.py, lines 7–46 · score 0.69 · MTA_LEFT, ERICA_LEFT, ERICA_RIGHT, gender, AD, MRI
  2. [2] § Results › Dementia prediction results with visual score system › SHAP feature attribution analysis ↔ clinical/config.py, lines 7–46 · score 0.53 · ERICA_LEFT, ERICA_RIGHT, AD, MTA, MRI
  3. [3] § Method › AI-based MRI diagnosis (AI-DX) model development ↔ clinical/train.py, lines 207–328 · score 0.52 · cross entropy loss, Adam, optimized, trained, classification, MRI
  4. [4] § Method › AI-based MRI diagnosis (AI-DX) model development ↔ dementia/train.py, lines 100–244 · score 0.52 · cross entropy loss, Adam, optimized, trained, classification, model
  5. [5] § Method › Deep learning model selection for dementia classification and visual score prediction ↔ visual_scores/train_mta_erica.py, lines 95–155 · score 0.51 · Cross Entropy Loss, Visual Score, ERICA, MTA, trained, model

Paper

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The authors' code

Python · 46 lines · 1.4 KB · MIT · 2 matches

  1. import os
  3. _dir = os.path.dirname(os.path.abspath(__file__))
  4. _repo_root = os.path.dirname(_dir)
  7. class Config:
  8. # Mode controls what inputs are combined:
  9. # 'clinical' – tabular clinical features only
  10. # 'scores_clinical' – visual scores + clinical features (no MRI)
  11. # 'mri_clinical' – MRI + clinical features
  12. # 'mri_scores_clinical' – MRI + visual scores + clinical features
  13. mode = 'mri_scores_clinical'
  15. # MRI backbone (only used in mri_* modes): 'resnet' or 'densenet'
  16. mri_backbone = 'resnet'
  18. num_classes = 3
  19. target_names = ['CN', 'MCI', 'AD']
  21. # Clinical feature columns present in the CSV (applied to all modes)
  22. clinical_features = ['EXAMAGE', 'GENDER', 'PTEDUCAT', 'CDR', 'FAQ', 'TMSE', 'MOCA']
  24. # Visual score columns (only used in scores_* modes)
  25. score_features = ['GCA', 'MTA_RIGHT', 'MTA_LEFT', 'ERICA_RIGHT', 'ERICA_LEFT']
  27. # Training
  28. world_size = 1
  29. batch_size = 8
  30. num_workers = 4
  31. pin_memory = True
  32. epochs = 30
  33. learning_rate = 1e-6
  34. weight_decay = 0.0005
  35. warmup_epochs = 0
  36. milestones = [30, 100]
  37. patient = 10
  39. # Paths
  40. weight_path = None
  41. version_name = 'v1'
  42. repo_root = _repo_root
  43. train_csv = os.path.join(_dir, 'train.csv')
  44. val_csv = os.path.join(_dir, 'val.csv')
  45. cache_dir = os.path.join(_dir, 'cache') # used in mri_* modes only
  46. root_dir = _dir

config.py at commit 2311ac7, under MIT · at the source

Overview

Authors: Lin Tun Naing1, Alisa Kunapinun1,2, Matthew N Dailey1, Rungsiri Patanasantichai3, Sittaya Buathong3, Chadaporn Keatmanee4, Jitsupa Wongsripuemtet3, Chatchawan Rattanabannakit5, Vorapun Senanarong5, Mongkol Ekpanyapong1, Dittapong Songsaeng3
ORCID iDs: Lin Tun Naing, Alisa Kunapinun
  1. Artificial Intelligence (AI) Center, Asian Institute of Technology, Pathumthani, 12120 Thailand
  2. Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, Florida 34946 USA
  3. Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700 Thailand
  4. Department of Computer Science, Ramkhamhaeng University, Bangkok, 10240 Thailand
  5. Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700 Thailand
Institutions: Asian Institute of Technology (Thailand); Florida Atlantic University (United States); Harbor Branch Oceanographic Institute (United States); Siriraj Hospital (Thailand); Mahidol University (Thailand); Ramkhamhaeng University (Thailand)
Journal: Scientific reports, volume 16, issue 1, article 21545
Dates: received 6 March 2025; accepted 29 April 2026; published online 11 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-51725-2 · PMID 42115238 · PMCID PMC13350691 · OpenAlex W4409465252
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.21203/rs.3.rs-6166115/v1
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Connectivity, Preprocessing, Statistics, Machine learning
Keywords: Dementia, Alzheimer, Visual scoring system, MTA, ERICA, GCA, Deep learning, Neurodegeneration, Machine learning, Predictive medicine
MeSH: Alzheimer Disease*, Artificial Intelligence*, Dementia*, Magnetic Resonance Imaging*, Aged, Aged, 80 and over, Brain, Cognitive Dysfunction, Convolutional Neural Networks, Deep Learning, Female, Humans, Intelligent Systems, Male, Severity of Illness Index (* major topic)
Topic: Brain Tumor Detection and Classification (Neurology, Neuroscience), according to OpenAlex
Funding: National Research Council of Thailand (N34A660390)
Citations: not cited yet (Europe PMC); 49 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

l-kuo/mri_visual_scores

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2311ac73959d219b967664079c24c5e2c908c8dc, 21 April 2026
Languages: Python (14)
Size: 40 files, 14 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (11 files), MONAI (7 files), NumPy (4 files), pandas (4 files), scikit-learn (4 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
16 files
At the source: github.com/l-kuo/mri_visual_scores

Code availability statement

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Read it in the paper: doi.org/10.1038/s41598-026-51725-2.

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 14 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability statement

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41598-026-51725-2.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 10 keywords, 15 MeSH terms, 1 funder, 34 references.

Cite

This paper

Naing, L. T., Kunapinun, A., Dailey, M. N., Patanasantichai, R., Buathong, S., Keatmanee, C., Wongsripuemtet, J., Rattanabannakit, C., Senanarong, V., Ekpanyapong, M., & Songsaeng, D. (2026). AI-powered evaluation of dementia severity based on clinical data and visual scoring systems (MTA, ERICA, GCA) from MRI. Scientific reports, 16(1), 21545. https://doi.org/10.1038/s41598-026-51725-2

BibTeX

@article{naing2026ai,
author = {Naing, Lin Tun and Kunapinun, Alisa and Dailey, Matthew N and Patanasantichai, Rungsiri and Buathong, Sittaya and Keatmanee, Chadaporn and Wongsripuemtet, Jitsupa and Rattanabannakit, Chatchawan and Senanarong, Vorapun and Ekpanyapong, Mongkol and Songsaeng, Dittapong},
title = {{AI-powered evaluation of dementia severity based on clinical data and visual scoring systems (MTA, ERICA, GCA) from MRI}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {21545},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-51725-2},
url = {https://doi.org/10.1038/s41598-026-51725-2},
pmid = {42115238},
pmcid = {PMC13350691}
}

RIS

TY - JOUR
AU - Naing, Lin Tun
AU - Kunapinun, Alisa
AU - Dailey, Matthew N
AU - Patanasantichai, Rungsiri
AU - Buathong, Sittaya
AU - Keatmanee, Chadaporn
AU - Wongsripuemtet, Jitsupa
AU - Rattanabannakit, Chatchawan
AU - Senanarong, Vorapun
AU - Ekpanyapong, Mongkol
AU - Songsaeng, Dittapong
TI - AI-powered evaluation of dementia severity based on clinical data and visual scoring systems (MTA, ERICA, GCA) from MRI
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/11
VL - 16
IS - 1
SP - 21545
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-51725-2
UR - https://doi.org/10.1038/s41598-026-51725-2
LA - en
ER -

CSL-JSON

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