Cardiac Sarcoidosis Classification with Deep Convolutional Neural Network-Based Features Using Polar Maps

Togo, Ren; Hirata, Kohji; Manabe, Osamu; Ohira, Hiroshi; Tsujino, Ichizo; Magota, Keiichi; Ogawa, Takahiro; Haseyama, Miki; Shiga, Tohru

doi:10.2139/ssrn.3237706

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…Machine learning (ML), including deep learning (DL) and ensemble learning, has been contemporarily used for the diagnosis and prognosis predictions of many eye diseases (11)(12)(13)(14). Numerous efforts have been made recently to accurately detect DME by ML based on OCT images due to continuous increase in the number of patients with DME (15)(16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning

Zhang¹,

Hu²,

Cao³

et al. 2021

Ann Transl Med

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Background: This study aimed to predict the treatment outcomes in patients with diabetic macular edema (DME) after 3 monthly anti-vascular endothelial growth factor (VEGF) injections using machine learning (ML) based on pretreatment optical coherence tomography (OCT) images and clinical variables.Methods: An ensemble ML system consisting of four deep learning (DL) models and five classical machine learning (CML) models was developed to predict the posttreatment central foveal thickness (CFT) and the best-corrected visual acuity (BCVA). A total of 363 OCT images and 7,587 clinical data records from 363 eyes were included in the training set (304 eyes) and external validation set (59 eyes). The DL models were trained using the OCT images, and the CML models were trained using the OCT images features and clinical variables. The predictive posttreatment CFT and BCVA values were compared with true outcomes obtained from the medical records.Results: For CFT prediction, the mean absolute error (MAE), root mean square error (RMSE), and R 2 of the best-performing model in the training set was 66.59, 93.73, and 0.71, respectively, with an area under receiver operating characteristic curve (AUC) of 0.90 for distinguishing the eyes with good anatomical response. The MAE, RMSE, and R 2 was 68.08, 97.63, and 0.74, respectively, with an AUC of 0.94 in the external validation set. For BCVA prediction, the MAE, RMSE, and R 2 of the best-performing model in the training set was 0.19, 0.29, and 0.60, respectively, with an AUC of 0.80 for distinguishing eyes with a good functional response. The external validation achieved a MAE, RMSE, and R 2 of 0.13, 0.20, and 0.68, respectively, with an AUC of 0.81.

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Section: Introductionmentioning

confidence: 99%

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning

Zhang¹,

Hu²,

Cao³

et al. 2021

Ann Transl Med

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“…Xu et al developed an automated pipeline using two cascaded V-NETs for lesion prediction and segmentation to detect multiple myeloma bone lesions from 68 Ga-Pentixafor PET/CT [131]. Togo et al demonstrated the feasibility of cardiac sarcoidosis detection from 18 F-FDG PET scans using Inception-v3 network (83.9% sensitivity and 87% specificity), which outperformed conventional SUV max -(46.8% sensitivity and 71.0% specificity) and coefficient of variance (CoV)-based (65.5% sensitivity and 75.0% specificity) approaches [132]. Ma et al modified a DenseNet architecture for the diagnosis of thyroid disease using SPECT images into three categories: Graves' disease, Hashimoto, and subacute thyroiditis [133].…”

Section: Ai-assisted Diagnosis and Prognosismentioning

confidence: 99%

The promise of artificial intelligence and deep learning in PET and SPECT imaging

et al. 2021

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This review sets out to discuss the foremost applications of artificial intelligence (AI), particularly deep learning (DL) algorithms, in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging. To this end, the underlying limitations/challenges of these imaging modalities are briefly discussed followed by a description of AI-based solutions proposed to address these challenges. This review will focus on mainstream generic fields, including instrumentation, image acquisition/formation, image reconstruction and low-dose/fast scanning, quantitative imaging, image interpretation (computer-aided detection/ diagnosis/prognosis), as well as internal radiation dosimetry. A brief description of deep learning algorithms and the fundamental architectures used for these applications is also provided. Finally, the challenges, opportunities, and barriers to full-scale validation and adoption of AI-based solutions for improvement of image quality and quantitative accuracy of PET and SPECT images in the clinic are discussed.

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“…18 F-FDG PET is also a common used mean of cardiac examination and it plays a fundamental role in diagnosis of cardiac sarcoidosis (CS) (25,130). A method based on deep convolutional neural network (DCNN) was proposed for CS classification (131).…”

Section: Application Of ML In Petmentioning

confidence: 99%

A review of the application of machine learning in molecular imaging

Yin¹,

Cao²,

Wang³

et al. 2021

Ann Transl Med

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Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI.OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural networkbased machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.

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Cardiac Sarcoidosis Classification with Deep Convolutional Neural Network-Based Features Using Polar Maps

Cited by 6 publications

References 12 publications

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning

The promise of artificial intelligence and deep learning in PET and SPECT imaging

A review of the application of machine learning in molecular imaging

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