ArticleScientific reports2021
Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed, 25 citations in OpenAlex.
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.Annals of nuclear medicine · 2025Article
- Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.European journal of nuclear medicine and molecular imaging · 2025Article
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance.European journal of nuclear medicine and molecular imaging · 2024Article
- Article
- Molecular Imaging in Parkinsonian Disorders-What's New and Hot?Brain sciences · 2022Review
- Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.European journal of nuclear medicine and molecular imaging · 2022Article
- The Legacy of the TTASAAN Report - Premature Conclusions and Forgotten Promises About SPECT Neuroimaging: A Review of Policy and Practice Part II.Frontiers in neurology · 2022Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study used explainable artificial intelligence for data-driven identification of extrastriatal brain regions that can contribute to the interpretation of dopamine transporter SPECT with
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.