Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.
Accurate prediction of pneumoconiosis is essential for individualized early prevention and treatment. However, the different manifestations and high heterogeneity among radiologists make it difficult to diagnose and stage pneumoconiosis accurately. Here, based on DR images collected from two centers...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 3025 - 3034 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283960&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283960 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283960 182283960 182283960 10.1007/s10278-024-01125-7 182283960 ppf: 3025 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists. aug: au: Wang, Yi Yan, Wanying Feng, Yibo Qian, Fang Zhang, Tiantian Huang, Xin Wang, Dawei Hu, Maoneng affil: https://ror.org/05mfr7w08 Imaging Center, The Third Clinical College of Hefei of Anhui Medical University, The Third People's Hospital of Hefei, Hefei, China sug: subj: Deep Learning Utilization Pneumoconiosis Radiography Diagnosis, Computer Assisted Digital Imaging Radiography, Thoracic Radiologists Funding Source Human Comparative Studies ROC Curve Validity Descriptive Statistics Sensitivity and Specificity Retrospective Design Male Female Adult Middle Age Aged Case Control Studies Machine Learning Algorithms Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Accurate prediction of pneumoconiosis is essential for individualized early prevention and treatment. However, the different manifestations and high heterogeneity among radiologists make it difficult to diagnose and stage pneumoconiosis accurately. Here, based on DR images collected from two centers, a novel deep learning model, namely Multi-scale Lesion-aware Attention Networks (MLANet), is proposed for diagnosis of pneumoconiosis, staging of pneumoconiosis, and screening of stage I pneumoconiosis. A series of indicators including area under the receiver operating characteristic curve, accuracy, recall, precision, and F1 score were used to comprehensively evaluate the performance of the model. The results show that the MLANet model can effectively improve the consistency and efficiency of pneumoconiosis diagnosis. The accuracy of the MLANet model for pneumoconiosis diagnosis on the internal test set, external validation set, and prospective test set reached 97.87%, 98.03%, and 95.40%, respectively, which was close to the level of qualified radiologists. Moreover, the model can effectively screen stage I pneumoconiosis with an accuracy of 97.16%, a recall of 98.25, a precision of 93.42%, and an F1 score of 95.59%, respectively. The built model performs better than the other four classification models. It is expected to be applied in clinical work to realize the automated diagnosis of pneumoconiosis digital chest radiographs, which is of great significance for individualized early prevention and treatment. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|