An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease

Arnaout, Rima; Curran, Lara; Zhao, Yili; Levine, Jami C.; Chinn, Erin; Moon-Grady, Anita J.

doi:10.1038/s41591-021-01342-5

Cited by 159 publications

(175 citation statements)

References 45 publications

Supporting

Mentioning

143

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, they have even shown advantages over commonly used inference-based statistical analysis methods in those databases [ 19 , 20 , 21 ]. In the clinical context, they are able to identify pathologic characteristics and even surpass human guidance in the detection of diseases [ 22 , 23 ]. Additionally, they might be able to reduce false-positive mistakes and differences in disease detection based on the different experience levels of the medical practitioners [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)

Dindorf

Konradi

Wolf

et al. 2021

Sensors

View full text Add to dashboard Cite

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively.

show abstract

Section: Introductionmentioning

confidence: 99%

Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)

Dindorf

Konradi

Wolf

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…A recent big data research developed an ensemble of neural networks to identify recommended cardiac views and diagnose complex CHD, which achieved a 95% sensitivity and 96% specificity. This research also demonstrated that the classifier made the decisions based on clinically relevant image features, and overcoming the problems of lack of experience as well as the poor quality of images were the key points in AI-aided diagnosis of CHD (36). However, artifacts, contour loss, noise, and uneven intensities always affect the feature recognization and analysis of fetal heart images (37).…”

Section: Intelligent Analysis and Disease Diagnosis By Fetal Echocardiographymentioning

confidence: 75%

Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications

Chen

Liu

et al. 2021

Front. Med.

View full text Add to dashboard Cite

Artificial intelligence (AI) can support clinical decisions and provide quality assurance for images. Although ultrasonography is commonly used in the field of obstetrics and gynecology, the use of AI is still in a stage of infancy. Nevertheless, in repetitive ultrasound examinations, such as those involving automatic positioning and identification of fetal structures, prediction of gestational age (GA), and real-time image quality assurance, AI has great potential. To realize its application, it is necessary to promote interdisciplinary communication between AI developers and sonographers. In this review, we outlined the benefits of AI technology in obstetric ultrasound diagnosis by optimizing image acquisition, quantification, segmentation, and location identification, which can be helpful for obstetric ultrasound diagnosis in different periods of pregnancy.

show abstract

“…An ensemble of neural networks, which was trained using 107,823 images from 1326 retrospective fetal cardiac US studies, could identify the recommended cardiac views as well as distinguish between normal hearts and complex congenital heart diseases. Segmentation models were also proposed to calculate standard fetal cardiothoracic measurements [132]. Komatsu et al proposed the CNN-based architecture known as supervised object detection with normal data only (SONO) to detect 18 cardiac substructures and structural abnormalities in fetal cardiac US videos.…”

Section: Obstetricsmentioning

confidence: 99%

“…ments [132]. Komatsu et al proposed the CNN-based architecture known as supervised object detection with normal data only (SONO) to detect 18 cardiac substructures and structural abnormalities in fetal cardiac US videos.…”

Section: Obstetricsmentioning

confidence: 99%

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Komatsu

Sakai

Dozen³

et al. 2021

Biomedicines

View full text Add to dashboard Cite

Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.

show abstract

An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease

Cited by 159 publications

References 45 publications

Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)

Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)

Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Contact Info

Product

Resources

About