Evidence map›Paper›PMID 41296269›Full record

ArticleAnnals of nuclear medicine2026

Examination of simple artificial intelligence-based analysis of dopamine transporter scintigraphy for supporting a diagnosis of Parkinson's disease.

Atsunori Murao, Kazuhiro Hara, Shintaro Oyama, Aya Ogura, Yoshiyuki Kishimoto, Mai Hatanaka, Naotoshi Fujita, Misaki Sato, Ikuko Aiba, Katsuhiko Kato and 1 more

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Article in Annals of nuclear medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Atsunori MuraoDepartment of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho Showa-Ku, Nagoya, Aichi, 466-8550, Japan.
Kazuhiro HaraDepartment of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho Showa-Ku, Nagoya, Aichi, 466-8550, Japan.
Shintaro OyamaInnovative Research Center for Preventive Medical Engineering, Institute of Innovation for Future Society, Nagoya University, Furo-cho Chikusa-Ku, Nagoya, Aichi, 464-8601, Japan.
Aya OguraDepartment of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho Showa-Ku, Nagoya, Aichi, 466-8550, Japan.
Yoshiyuki KishimotoDepartment of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho Showa-Ku, Nagoya, Aichi, 466-8550, Japan.
Mai HatanakaDepartment of Neurology, TOYOTA Memorial Hospital, 1-1 Heiwa-cho, Toyota, Aichi, 471-8513, Japan.
Naotoshi FujitaDepartment of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho Showa-Ku, Nagoya, Aichi, 466-8550, Japan.
Misaki SatoDepartment of Neurology, NHO Higashinagoya National Hospital, 5-101 Umemorizaka Meito-Ku, Nagoya, Aichi, 465-8620, Japan.
Ikuko AibaDepartment of Neurology, NHO Higashinagoya National Hospital, 5-101 Umemorizaka Meito-Ku, Nagoya, Aichi, 465-8620, Japan.
Katsuhiko KatoFunctional Medical Imaging, Biomedical Imaging Sciences, Division of Advanced Information Health Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daikominami Higashi-Ku, Aichi, 461-8673, Nagoya, Japan.
Masahisa KatsunoDepartment of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho Showa-Ku, Nagoya, Aichi, 466-8550, Japan. katsuno.masahisa.i1@f.mail.nagoya-u.ac.jp.ORCID http://orcid.org/0000-0001-9453-9311

Funding

Japan Society for the Promotion of Science (JSPS) KAKENHI 24K10638MHLW Research on rare and intractable diseases Program JPMH23FC1008
6 · The paper itself

Abstract

introductionIn the Movement Disorder Society criteria for the diagnosis of Parkinson's disease (PD), evaluation of the presynaptic dopamine system should be performed using dopamine transporter single-photon emission computed tomography (DAT SPECT). However, it is difficult for unexperienced physicians to detect a mild defect. Here, we attempted to develop a simple deep learning-based image analysis method to evaluate DAT SPECT images.

methodsWe used data from 300 patients who were diagnosed with PD and 102 control patients with non-neurodegenerative diseases as the artificial intelligence (AI) development cohort. For validation, we analyzed the data of 96 patients with PD from an independent cohort. We divided the development cohort into the training and test sets. Using the training set, we performed transfer learning using six pre-trained convolutional neural network architectures, and created AI models. We evaluated their accuracy, sensitivity, and area under the receiver operating characteristic curve, and further confirmed their validity by using the validation cohort. In addition, we compared the accuracy of the best AI model with that of two experienced neurologists and a resident.

resultsThe selected AI model could interpret DAT SPECT images with an accuracy of 0.959; accuracy in the validation cohort was 0.8959-1. There was no significant difference between the accuracy of the AI model and physicians.

conclusionOur simple AI model for the interpretation of DAT SPECT images was accurate and robust. Its accuracy was equivalent to that of physicians.

Indexed as

Artificial IntelligenceDopamine Plasma Membrane Transport ProteinsImage Processing, Computer-AssistedParkinson DiseaseTomography, Emission-Computed, Single-PhotonAgedFemaleHumansMaleMiddle AgedDopamine Plasma Membrane Transport ProteinsAI modelConvolutional neural networkDeep learningDopamine transporter scintigraphyParkinson’s disease

Identifiers

PMID41296269

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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.