ArticleJapanese journal of radiology2018
Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences.
Article in Japanese journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Automated PET tracer classification in multi-site, multi-cohort studies using deep learning.EJNMMI research · 2026Article
- Optimizing MRI sequence classification performance: insights from domain shift analysis.European radiology · 2025Article
- Automated Classification of Body MRI Sequences Using Convolutional Neural Networks.Academic radiology · 2025Article
- MRISeqClassifier: A Deep Learning Toolkit for Precise MRI Sequence Classification.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Classification of Multi-Parametric Body MRI Series Using Deep Learning.IEEE journal of biomedical and health informatics · 2024Article
- Article
- Algorithms for classification of sequences and segmentation of prostate gland: an external validation study.Abdominal radiology (New York) · 2024Article
- Article
- The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review.Przeglad gastroenterologiczny · 2024Review
- Deep Learning-based Identification of Brain MRI Sequences Using a Model Trained on Large Multicentric Study Cohorts.Radiology. Artificial intelligence · 2024Article
- A Lightweight Deep Learning Framework for Automatic MRI Data Sorting and Artifacts Detection.Journal of medical systems · 2023Article
- Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Analysis of "visible in retrospect" to monitor false-negative findings in radiological reports.Japanese journal of radiology · 2023Article
- Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.Cancers · 2022Review
- Automated Protocoling for MRI Exams-Challenges and Solutions.Journal of digital imaging · 2022Review
- Classifying the Acquisition Sequence for Brain MRIs Using Neural Networks on Single Slices.Cureus · 2022Article
- A fundamental study assessing the generalized fitting method in conjunction with every possible coalition of N-combinations (G-EPOC) using the appendicitis detection task of computed tomography.Polish journal of radiology · 2021Article
- Introduction to deep learning: minimum essence required to launch a research.Japanese journal of radiology · 2020Article
- A Fundamental Study Assessing the Diagnostic Performance of Deep Learning for a Brain Metastasis Detection Task.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2020Article
- Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study.Journal of magnetic resonance imaging : JMRI · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
purposeThe confusion of MRI sequence names could be solved if MR images were automatically identified after image data acquisition. We revealed the ability of deep learning to classify head MRI sequences. MATERIALS AND
methodsSeventy-eight patients with mild cognitive impairment (MCI) having apparently normal head MR images and 78 intracranial hemorrhage (ICH) patients with morphologically deformed head MR images were enrolled. Six imaging protocols were selected to be performed: T2-weighted imaging, fluid attenuated inversion recovery imaging, T2-star-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient mapping, and source images of time-of-flight magnetic resonance angiography. The proximal first image slices and middle image slices having ambiguous and distinctive contrast patterns, respectively, were classified by two deep learning imaging classifiers, AlexNet and GoogLeNet.
resultsAlexNet had accuracies of 73.3%, 73.6%, 73.1%, and 60.7% in the middle slices of MCI group, middle slices of ICH group, first slices of MCI group, and first slices of ICH group, while GoogLeNet had accuracies of 100%, 98.1%, 93.1%, and 94.8%, respectively. AlexNet significantly had lower classification ability than GoogLeNet for all datasets.
conclusionsGoogLeNet could judge the types of head MRI sequences with a small amount of training data, irrespective of morphological or contrast conditions.
Indexed as
Identifiers
30232585What 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.