Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study

Diagnostics, Год журнала: 2022, Номер 12(11), С. 2724 - 2724

Опубликована: Ноя. 7, 2022

In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However, current workload in extensive health care facilities and lack of well-trained radiologists is a significant challenge patient pathway. Therefore, an accurate, reliable, fast computer-aided diagnosis (CAD) system capable detecting abnormalities crucial improving radiological workflow. this prospective multicenter quality-improvement study, we have evaluated whether artificial intelligence (AI) can be used as X-ray screening tool real clinical settings. Methods: A team AI-based (qXR) part their daily reporting routine to report consecutive for multicentre study. This study took place large radiology network India between June 2021 March 2022. Results: total 65,604 were processed during period. The overall performance AI achieved normal abnormal was good. high negatively predicted value (NPV) 98.9% achieved. terms area under curve (AUC), NPV corresponding subabnormalities obtained blunted CP angle (0.97, 99.5%), hilar dysmorphism (0.86, 99.9%), cardiomegaly (0.96, 99.7%), reticulonodular pattern (0.91, rib fracture (0.98, scoliosis atelectasis calcification consolidation (0.95, 99.6%), emphysema fibrosis nodule 99.8%), opacity (0.92, 99.2%), pleural effusion pneumothorax (0.99, 99.9%). Additionally, turnaround time (TAT) decreased by about 40.63% from pre-qXR period post-qXR Conclusions: solution screened assisted ruling out patients with confidence, thus allowing focus more on assessing pathology treatment pathways.

Язык: Английский

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Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data

Journal of Digital Imaging, Год журнала: 2023, Номер 36(4), С. 1332 - 1347

Опубликована: Март 29, 2023

The current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis for real-world data, which always present a long-tail distribution. To tackle this issue, public dataset, "ChestX-ray14," involved fourteen (14) disease labels, was randomly divided into the train, validation, and test sets with ratios of 0.7, 0.1, 0.2. Two pretrained state-of-the-art networks, EfficientNet-b5 CoAtNet-0-rw, were chosen as backbones. After fully-connected layer, final layer 14 sigmoid activation units added to output each disease's diagnosis. achieve better adaptive learning, novel loss (Lours) designed, coalesced reweighting tail sample focus. For comparison, ResNet50 network weighted binary cross-entropy (LWBCE) used baseline, showed best performance previous study. overall individual areas under receiver operating curve (AUROC) label evaluated compared among different models. Group-score-weighted class mapping (Group-CAM) is applied visual interpretations. As result, CoAtNet-0-rw + Lours AUROC 0.842, significantly higher than LWBCE (AUROC: 0.811, p = 0.037). Group-CAM presented that model could pay proper attention lesions most labels (e.g., atelectasis, edema, effusion) but wrong other such pneumothorax; meanwhile, mislabeling dataset found. Overall, study an advanced AI diagnostic achieving significant improvement chest X-rays, particularly data challenging distributions.

Язык: Английский

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AI Based Diagnosis of Pneumonia

Wireless Personal Communications, Год журнала: 2022, Номер 126(4), С. 3677 - 3692

Опубликована: Июнь 29, 2022

Язык: Английский

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MULTI-LABEL CLASSIFICATION OF THORACIC DISEASES USING STRUCTURED DEEP LEARNING FRAMEWORK

Biomedical Engineering Applications Basis and Communications, Год журнала: 2024, Номер 36(02)

Опубликована: Фев. 16, 2024

The current medical scenario indicates that thoracic diseases are the primary cause of illnesses human beings worldwide. COVID-19 outbreak results in generation tremendous Chest X-Ray (CXR) and Computed Tomography (CT) image data archives because CXR CT imaging foremost effective screening tools characterize patterns features pathologies present lungs. After careful observation, these chest radiographs show images often labeled with more than one pathology, extends disease diagnosis problem to tedious multi-label classification task. So, this paper proposes a modified mish activation function compound loss-based Separable Convolution Neural Network (SCNN) model accomplishes image-level detection multiple from multi-modality radiographs. We investigate power separable convolution CNN network interprets spatial depth-wise dimensions each pixel images. A called as Swmish, non-monotonic function, is introduced preserves negative gradient flow input capture fine-grained detail regions. Moreover, loss dice coefficient binary cross entropy applied strongly optimizes during training. generality proposed SCNN confirmed after conducting comprehensive experiments on COVID19-CT datasets. gains an AUC score 0.91 0.13 dataset achieves testing accuracy 0.97, 0.99 0.11 dataset. These highest evaluation metrics classifying abnormalities

Язык: Английский

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A Systematic Review: Classification of Lung Diseases from Chest X-Ray Images Using Deep Learning Algorithms

SN Computer Science, Год журнала: 2024, Номер 5(4)

Опубликована: Апрель 6, 2024

Язык: Английский

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