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ردیف | عنوان | نوع |
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1 |
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
طراحی یادگیری عمیق خودکار برای طبقه بندی تصویر پزشکی توسط متخصصان مراقبت های بهداشتی و بدون تجربه برنامه نویسی: یک مطالعه امکان سنجی-2019 Background Deep learning has the potential to transform health care; however, substantial expertise is required to
train such models. We sought to evaluate the utility of automated deep learning software to develop medical image
diagnostic classifiers by health-care professionals with no coding—and no deep learning—expertise.
Methods We used five publicly available open-source datasets: retinal fundus images (MESSIDOR); optical coherence
tomography (OCT) images (Guangzhou Medical University and Shiley Eye Institute, version 3); images of skin
lesions (Human Against Machine [HAM] 10000), and both paediatric and adult chest x-ray (CXR) images (Guangzhou
Medical University and Shiley Eye Institute, version 3 and the National Institute of Health [NIH] dataset, respectively)
to separately feed into a neural architecture search framework, hosted through Google Cloud AutoML, that
automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity, and
positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative
performance was assessed using the area under the precision recall curve (AUPRC). In the case of the deep learning
model developed on a subset of the HAM10000 dataset, we did external validation using the Edinburgh Dermofit
Library dataset.
Findings Diagnostic properties and discriminative performance from internal validations were high in the binary
classification tasks (sensitivity 73·3–97·0%; specificity 67–100%; AUPRC 0·87–1·00). In the multiple classification
tasks, the diagnostic properties ranged from 38% to 100% for sensitivity and from 67% to 100% for specificity. The
discriminative performance in terms of AUPRC ranged from 0·57 to 1·00 in the five automated deep learning
models. In an external validation using the Edinburgh Dermofit Library dataset, the automated deep learning model
showed an AUPRC of 0·47, with a sensitivity of 49% and a positive predictive value of 52%.
Interpretation All models, except the automated deep learning model trained on the multilabel classification task of
the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art
performing deep learning algorithms. The performance in the external validation study was low. The quality of the
open-access datasets (including insufficient information about patient flow and demographics) and the absence of
measurement for precision, such as confidence intervals, constituted the major limitations of this study. The
availability of automated deep learning platforms provide an opportunity for the medical community to enhance
their understanding in model development and evaluation. Although the derivation of classification models without
requiring a deep understanding of the mathematical, statistical, and programming principles is attractive,
comparable performance to expertly designed models is limited to more elementary classification tasks. Furthermore,
care should be placed in adhering to ethical principles when using these automated models to avoid discrimination
and causing harm. Future studies should compare several application programming interfaces on thoroughly
curated datasets. |
مقاله انگلیسی |
2 |
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
مقایسه عملکرد یادگیری عمیق در برابر متخصصان مراقبت های بهداشتی در تشخیص بیماری ها از تصویربرداری پزشکی: یک مرور منظم و متاآنالیز-2019 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. |
مقاله انگلیسی |