AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation.
The objective of this study is to evaluate the feasibility of a disease-specific deep learning (DL) model based on minimum intensity projection (minIP) for automated emphysema detection in low-dose computed tomography (LDCT) scans. LDCT scans of 240 individuals from a population-based cohort in the...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 3; pp. 538 - 551 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2022
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| 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=157184688&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157184688 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2022 vid: 35 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157184688 155316059 157184688 157184688 10.1007/s10278-022-00599-7 157184688 ppf: 538 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation. aug: au: Nagaraj, Yeshaswini Wisselink, Hendrik Joost Rook, Mieneke Cai, Jiali Nagaraj, Sunil Belur Sidorenkov, Grigory Veldhuis, Raymond Oudkerk, Matthijs Vliegenthart, Rozemarijn van Ooijen, Peter affil: Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands sug: subj: Emphysema Diagnosis Contrast Media Administration and Dosage Tomography, X-Ray Computed Neural Networks (Computer) Artificial Intelligence Utilization Deep Learning Utilization Human Netherlands Female Male Middle Age Aged Prospective Studies Retrospective Design Validity Automation Early Diagnosis Cancer Screening ROC Curve Sensitivity and Specificity Descriptive Statistics Middle Aged: 45-64 years Aged: 65+ years Female Male ab: The objective of this study is to evaluate the feasibility of a disease-specific deep learning (DL) model based on minimum intensity projection (minIP) for automated emphysema detection in low-dose computed tomography (LDCT) scans. LDCT scans of 240 individuals from a population-based cohort in the Netherlands (ImaLife study, mean age ± SD = 57 ± 6 years) were retrospectively chosen for training and internal validation of the DL model. For independent testing, LDCT scans of 125 individuals from a lung cancer screening cohort in the USA (NLST study, mean age ± SD = 64 ± 5 years) were used. Dichotomous emphysema diagnosis based on radiologists' annotation was used to develop the model. The automated model included minIP processing (slab thickness range: 1 mm to 11 mm), classification, and detection maps generation. The data-split for the pipeline evaluation involved class-balanced and imbalanced settings. The proposed DL pipeline showed the highest performance (area under receiver operating characteristics curve) for 11 mm slab thickness in both the balanced (ImaLife = 0.90 ± 0.05) and the imbalanced dataset (NLST = 0.77 ± 0.06). For ImaLife subcohort, the variation in minIP slab thickness from 1 to 11 mm increased the DL model's sensitivity from 75 to 88% and decreased the number of false-negative predictions from 10 to 5. The minIP-based DL model can automatically detect emphysema in LDCTs. The performance of thicker minIP slabs was better than that of thinner slabs. LDCT can be leveraged for emphysema detection by applying disease specific augmentation. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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