Deep learning in the ultrasound evaluation of neonatal respiratory status

Gravina, Michela; Gragnaniello, Diego; Verdoliva, Luisa; Poggi, Gianfranco; Corsini, Iuri; Dani, Carlo; Meneghin, Fabio; Lista, Gianluca; Aversa, Salvatore; Raimondi, Francesco; Migliaro, Fiorella; Sansone, Carlo

doi:10.1109/icpr48806.2021.9412732

Cited by 3 publications

(3 citation statements)

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“…The best CNN model established by the team had a sensitivity of 93% and a specificity of 96% when determining the presence of B-line ( 28 ). Gravina et al ( 29 ) collected neonatal lung ultrasound images and videos to construct different CNN models to diagnose and differentially diagnose temporary tachypnea in neonates and neonatal respiratory distress syndrome, the highest accuracy of which was 87.8%. AI automated image analysis has advantages in its ability to assist doctors in improving the accuracy, efficiency and work intensity.…”

Section: Discussionmentioning

confidence: 99%

“…A stronger rotation would give an unnatural image; thus, it is avoided ( 13 ). Finally, to simulate multiple acquisitions of the same image with multiple devices, and thus make the training procedure robust against different calibrations and instruments, we randomly modified the brightness and the contrast of the images in a relative range of 25% ( 14 ).…”

Section: Objects and Methodsmentioning

confidence: 99%

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Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network-based transfer learning

et al. 2022

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BackgroundStudies show that lung ultrasound (LUS) can accurately diagnose community-acquired pneumonia (CAP) and keep children away from radiation, however, it takes a long time and requires experienced doctors. Therefore, a robust, automatic and computer-based diagnosis of LUS is essential.ObjectiveTo construct and analyze convolutional neural networks (CNNs) based on transfer learning (TL) to explore the feasibility of ultrasound image diagnosis and grading in CAP of children.Methods89 children expected to receive a diagnosis of CAP were prospectively enrolled. Clinical data were collected, a LUS images database was established comprising 916 LUS images, and the diagnostic values of LUS in CAP were analyzed. We employed pre-trained models (AlexNet, VGG 16, VGG 19, Inception v3, ResNet 18, ResNet 50, DenseNet 121 and DenseNet 201) to perform CAP diagnosis and grading on the LUS database and evaluated the performance of each model.ResultsAmong the 89 children, 24 were in the non-CAP group, and 65 were finally diagnosed with CAP, including 44 in the mild group and 21 in the severe group. LUS was highly consistent with clinical diagnosis, CXR and chest CT (kappa values = 0.943, 0.837, 0.835). Experimental results revealed that, after k-fold cross-validation, Inception v3 obtained the best diagnosis accuracy, PPV, sensitivity and AUC of 0.87 ± 0.02, 0.90 ± 0.03, 0.92 ± 0.04 and 0.82 ± 0.04, respectively, for our dataset out of all pre-trained models. As a result, best accuracy, PPV and specificity of 0.75 ± 0.03, 0.89 ± 0.05 and 0.80 ± 0.10 were achieved for severity classification in Inception v3.ConclusionsLUS is a reliable method for diagnosing CAP in children. Experiments showed that, after transfer learning, the CNN models successfully diagnosed and classified LUS of CAP in children; of these, the Inception v3 achieves the best performance and may serve as a tool for the further research and development of AI automatic diagnosis LUS system in clinical applications.Registrationwww.chictr.org.cn ChiCTR2200057328.

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Section: Discussionmentioning

confidence: 99%

Section: Objects and Methodsmentioning

confidence: 99%

Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network-based transfer learning

et al. 2022

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“…Gravina et al [14] , in 2021, employed a deep learning approach in classifying ARDS, Transient Tachypnea (TTN) and healthy ultrasound scans. Five Neural Networks (NN) were deployed respectively.…”

Section: Related Workmentioning

confidence: 99%

An Interpretable Neonatal Lung Ultrasound Feature Extraction and Lung Sliding Detection System Using Object Detectors

Bassiouny,

Mohamed,

Umapathy

et al. 2024

IEEE J. Transl. Eng. Health Med.

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The objective of this study was to develop an interpretable system that could detect specific lung features in neonates. A challenging aspect of this work was that normal lungs showed the same visual features (as that of Pneumothorax (PTX)). M-mode is typically necessary to differentiate between the two cases, but its generation in clinics is time-consuming and requires expertise for interpretation, which remains limited. Therefore, our system automates M-mode generation by extracting Regions of Interest (ROIs) without human in the loop. Object detection models such as faster Region Based Convolutional Neural Network (fRCNN) and RetinaNet models were employed to detect seven common Lung Ultrasound (LUS) features. fRCNN predictions were then stored and further used to generate M-modes. Beyond static feature extraction, we used a Hough transform based statistical method to detect “lung sliding” in these M-modes. Results showed that fRCNN achieved a greater mean Average Precision (mAP) of 86.57% (Intersection-over-Union (IoU) = 0.2) than RetinaNet, which only displayed a mAP of 61.15%. The calculated accuracy for the generated RoIs was 97.59% for Normal videos and 96.37% for PTX videos. Using this system, we successfully classified 5 PTX and 6 Normal video cases with 100% accuracy. Automating the process of detecting seven prominent LUS features addresses the time-consuming manual evaluation of Lung ultrasound in a fast paced environment. Clinical impact: Our research work provides a significant clinical impact as it provides a more accurate and efficient method for diagnosing lung diseases in neonates.

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Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network based transfer learning (Preprint)

Fang¹,

Li²,

Li³

et al. 2022

Preprint

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BACKGROUND Studies show that lung ultrasound (LUS) can accurately diagnose pneumonia in children, but intelligent diagnosis hasn’t been explored in this area. OBJECTIVE To construct deep learning (DL) models based on transfer learning (TL) to explore the feasibility of ultrasound image diagnosis and grading in community-acquired pneumonia (CAP) of children. METHODS From September 2021 to February 2022, 89 inpatients who were expected to receive a diagnosis of CAP in the pediatric ward of local hospital were prospectively enrolled. Clinical data were collected, a LUS images database was established, and the diagnostic values of LUS in CAP were analyzed. We constructed DL models using AlexNet, ResNet-18 and ResNet-50 to perform CAP diagnosis and grading on the LUS database and evaluated the performance of each model. The models were trained separately with transfer learning. RESULTS 1. Among the 89 children, 24 were in the non-CAP group, and 65 were finally diagnosed with CAP, including 44 in the mild group and 21 in the severe group. 2. LUS was highly consistent with clinical diagnosis, CXR and chest CT (kappa values = 0.943, 0.837, 0.835). 3. In the task of diagnosing CAP in children, different ratios of training and test sets (5:5; 8:2; 9:1) affected the performance of the model. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve of the AlexNet model ranged from 87.6%–89.5%, 93.2%–97.1%, 68.4%–73.6%, 89.5%–90.7%, 79.5%–89.9%, and 0.820–0.841; for the ResNet-18 model, these values were 87.3%–92.4%, 94.4%–98.3%, 64.6%–76.1%, 89.4%–91.8%, 83.7%–94.9%, and 0.795–0.972; moreover, for the ResNet-50 model, these values were 88.2%–90.9%, 94.0%–97.9%, 70.8%–72.1%, 90.2%–91.0%, 81.3%–92.6%, and 0.824–0.850. 4. When the training set and test set ratio was 8:2, the AlexNet, ResNet-18, and ResNet-50 models were highly consistent with the manual diagnosis CAP (kappa values = 0.832, 0.848, and 0.847 respectively), which was comparable to CXR and chest CT, and the ResNet-18 model performed better than manual ultrasound diagnosis (P=0.021). 5. In the task of grading, the accuracy of all three models increased with additions to the training set. When the ratio was 9:1, the accuracy of the ResNet-18 model reached 96%. CONCLUSIONS LUS is a reliable method for diagnosing CAP in children. The transfer learning-based DL models AlexNet, ResNet-18 and ResNet-50 perform well in children’s CAP diagnosis in the database we established; of these, the ResNet-18 model achieves the best performance and may serve as a tool for the further research and development of AI automatic diagnosis LUS system in clinical applications. CLINICALTRIAL This study was a prospective case-control clinical diagnostic study, which was reviewed by the clinical trial registration website www.chictr.org.cn and obtained the registration number ChiCTR2200057328.

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Deep learning in the ultrasound evaluation of neonatal respiratory status

Cited by 3 publications

References 25 publications

Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network-based transfer learning

Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network-based transfer learning

An Interpretable Neonatal Lung Ultrasound Feature Extraction and Lung Sliding Detection System Using Object Detectors

Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network based transfer learning (Preprint)

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