Retracted: An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson's disease using SPECT images

Mohammed, Farhan; He, Xiangjian; Lin, Yan

doi:10.1016/j.compmedimag.2020.101810

Cited by 29 publications

(21 citation statements)

References 15 publications

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“…Furthermore, for imaging dataset including MRI, PET CT, and DaTSCAN were mainly obtained from Parkinson Progression Markers Initiative (PPMI) to train classifier, as seen in [ 20 , 28 , 41 , 47 , 59 , 66 , 67 , 76 , 82 , 86 , 88 , 90 , 94 , 95 ]; hence, among all studies, CNN in [ 20 ] and FNN in [ 28 ] achieved an outstanding result for image classification.…”

Section: Discussionmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

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Section: Discussionmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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“…The evaluation on 56 normal and abnormal patients exhibited a left ventricular volume correlation coefficient of 0.910 ± 0.061 between AI-and physicians-based analysis. The diagnosis of Parkinson's disease from brain SPECT scans using deep learning approaches has been investigated in [142], wherein 2723 patients from healthy and Parkinson's disease groups were examined. The deep learning approach demonstrated outstanding performance with a sensitivity of 99.04%, specificity of 99.63%, and accuracy of 99.34%, suggesting the remarkable potential in the diagnosis of Parkinson's disease and its management.…”

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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“…A set of images was retrieved from the Parkinson's Progression Markers Initiative (PPMI) database [9]. This data set is used by many earlier DaTSCAN classifying studies [4][5][6][7] and is useful as a multi-site study data set from varying sources and for benchmarking. PPMI is a longitudinal study designed to assess the progression of PD using clinical features, biological markers and imaging data [9].…”

Section: Data Parkinson's Progression Markers Initiative Ppmimentioning

confidence: 99%

“…A CNN model, named DaTNet-3, was designed (summarized in Fig. 1), partly derived from the network by Mohammed et al [4], which in itself is a modi ed version of the AlexNet [12].…”

Section: Convolutional Neural Network Architecturementioning

confidence: 99%

“…Varying inter-observer variability of human readers and the dependence on experienced nuclear medicine physicians make DaTSCAN [2,3] interpretation an interesting task for arti cial intelligence (AI) assisted classi cation. In literature several machine learning algorithms regarding this task are reported and show good performance [4][5][6][7]. However, there is a lack of studies evaluating the performance of training and testing on sizable clinical data sets.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence-Based Assistance in Clinical 123I-FP-CIT SPECT Scan Interpretation

Wolfswinkel

Wielaard

Lavalaye

et al. 2021

Preprint

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Purpose: Dopamine transporter (DAT) imaging with 123I-FP-CIT SPECT is used to support the diagnosis of Parkinson’s disease (PD) in clinically uncertain cases. Previous studies showed that automatic classification of 123I‑FP‑CIT SPECT images (marketed as DaTSCAN) is feasible by using machine learning algorithms. However, these studies lacked sizable use of data from routine clinical practice. This study aims to contribute to the discussion whether artificial intelligence (AI) can be applied in clinical practice. Moreover, we investigated the need for hospital specific training data.Methods: A convolutional neural network (CNN) named DaTNet-3 was designed and trained to classify DaTSCAN images as either normal or supportive of a dopaminergic deficit. Both a multi-site data set (n = 2412) from the Parkinson’s Progression Marker Initiative (PPMI) and an in-house data set containing clinical images (n = 932) obtained in routine practice at the St Antonius hospital (STA) were used for training and testing. STA images were labeled based on interpretation by nuclear medicine physicians. To investigate whether indeterminate scans effects classification accuracy, a threshold was applied on the output probability.Results: DaTNet-3 trained with STA data reached an accuracy of 89.0% in correctly identifying images of the clinical STA test set as either normal or with decreased striatal DAT binding (98.5% on the PPMI test set). When thresholded, accuracy increased to 95.7%. This increase was not observed when trained with PPMI data, indicating the incorrect images were confidently classified as the incorrect class.Conclusion: Based on results of DaTNet-3 we conclude that automatic interpretation of DaTSCAN images with AI is feasible and robust. Further, we conclude DaTNet-3 performs slightly better when it is trained with hospital specific data. This difference increased when output probability was thresholded. Therefore we conclude that the usability of a data set increases if it contains indeterminate images.

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Retracted: An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson's disease using SPECT images

Cited by 29 publications

References 15 publications

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

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

Artificial Intelligence-Based Assistance in Clinical 123I-FP-CIT SPECT Scan Interpretation

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