Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT

Shibutani, Takayuki; Nakajima, Kénichi; Wakabayashi, Hiroshi; Mori, Hiroshi; Matsuo, Shinro; Yoneyama, Hiroto; Konishi, Teruko; Okuda, Koichi; Onoguchi, Masahisa; Kinuya, Seigo

doi:10.1007/s12149-018-1306-4

Cited by 16 publications

(21 citation statements)

References 23 publications

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“…Of the 69 studies, 25 studies did an out-of-sample external validation and were therefore included in a meta-analysis. 21,28,30,34,[36][37][38][39]43,[53][54][55][56]61,[65][66][67]70,73,74,79,81,90,91,99 In line with the aims of this review, all eligible studies were included regardless of the target condition. The meta-analysis therefore included diagnostic classifications in multiple specialty areas, including ophthalmology (six studies), breast cancer (three studies), lung cancer (two studies), dermatological cancer (three studies), trauma and orthopaedics (two studies), respiratory disease (two studies), Of these 25 studies, only 14 used the same sample for the out-of-sample validation to compare performance between deep learning algorithms and health-care professionals, with 31 contingency tables for deep learning algorithm performance and 54 tables for healthcare professionals (figure 3).…”

Section: Resultsmentioning

confidence: 99%

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

Liu

Faes

Kale

et al. 2019

The Lancet Digital Health

1,094

794

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Background Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging.Methods In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase, Science Citation Index, and Conference Proceedings Citation Index for studies published from Jan 1, 2012, to June 6, 2019. Studies comparing the diagnostic performance of deep learning models and health-care professionals based on medical imaging, for any disease, were included. We excluded studies that used medical waveform data graphics material or investigated the accuracy of image segmentation rather than disease classification. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. Studies undertaking an out-of-sample external validation were included in a meta-analysis, using a unified hierarchical model. This study is registered with PROSPERO, CRD42018091176.Findings Our search identified 31 587 studies, of which 82 (describing 147 patient cohorts) were included. 69 studies provided enough data to construct contingency tables, enabling calculation of test accuracy, with sensitivity ranging from 9•7% to 100•0% (mean 79•1%, SD 0•2) and specificity ranging from 38•9% to 100•0% (mean 88•3%, SD 0•1). An out-of-sample external validation was done in 25 studies, of which 14 made the comparison between deep learning models and health-care professionals in the same sample. Comparison of the performance between health-care professionals in these 14 studies, when restricting the analysis to the contingency table for each study reporting the highest accuracy, found a pooled sensitivity of 87•0% (95% CI 83•0-90•2) for deep learning models and 86•4% (79•9-91•0) for health-care professionals, and a pooled specificity of 92•5% (95% CI 85•1-96•4) for deep learning models and 90•5% (80•6-95•7) for health-care professionals.Interpretation Our review found the diagnostic performance of deep learning models to be equivalent to that of health-care professionals. However, a major finding of the review is that few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample. Additionally, poor reporting is prevalent in deep learning studies, which limits reliable interpretation of the reported diagnostic accuracy. New reporting standards that address specific challenges of deep learning could improve future studies, enabling greater confidence in the results of future evaluations of this promising technology.

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Section: Resultsmentioning

confidence: 99%

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

Liu

Faes

Kale

et al. 2019

The Lancet Digital Health

1,094

794

View full text Add to dashboard Cite

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“…The results revealed that single-vessel CAD was more difficult to identify. Recently, complementary work by Shibutani et al (61), including per-segment analysis, was performed on 21 patients who underwent perfusion SPECT. A total of 109 abnormal regions were examined and an ANN achieved better results than two independent observers for stress defect and ischemia detection, with respect to ICA as gold standard.…”

Section: Coronary Artery Diseasementioning

confidence: 99%

Image-Based Cardiac Diagnosis With Machine Learning: A Review

Martín-Isla

Campello

Izquierdo

et al. 2020

Front. Cardiovasc. Med.

148

118

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Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.

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“…The main outcome of the diagnostic models was classified into three formats: binary (e.g., the status of CAD, yes or no) (34/46, 74.1%), ordinal (e.g., severity grading of CAD) (8/46, 17.3%) (16,19,(29)(30)(31)(32)(33)55), and multinomial (e.g., multiple diseases or classification of CAD) (4/46, 8.6%) (18,46,50,51).…”

Section: Outcome and Reference Standards In The Included Studiesmentioning

confidence: 99%

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Wang

Wang²,

Wang³

et al. 2022

Front. Cardiovasc. Med.

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BackgroundCoronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers.ObjectiveWe aim to investigate and summarize how AI algorithms are used in the development of diagnostic models of CAD with imaging markers.MethodsThis scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Eligible articles were searched in PubMed and Embase. Based on the predefined included criteria, articles on coronary heart disease were selected for this scoping review. Data extraction was independently conducted by two reviewers, and a narrative synthesis approach was used in the analysis.ResultsA total of 46 articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomography (15/46, 32.6%) and coronary computed tomography angiography (15/46, 32.6%). Deep learning (DL) (41/46, 89.2%) algorithms were used more often than machine learning algorithms (5/46, 10.8%). The models yielded good model performance in terms of accuracy, sensitivity, specificity, and AUC. However, most of the primary studies used a relatively small sample (n < 500) in model development, and only few studies (4/46, 8.7%) carried out external validation of the AI model.ConclusionAs non-invasive diagnostic methods, imaging markers integrated with AI have exhibited considerable potential in the diagnosis of CAD. External validation of model performance and evaluation of clinical use aid in the confirmation of the added value of markers in practice.Systematic review registration[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022306638], identifier [CRD42022306638].

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Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT

Cited by 16 publications

References 23 publications

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

Image-Based Cardiac Diagnosis With Machine Learning: A Review

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

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