Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment.
In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leveraged 496 clinical MRI exams collected retrospectivel...
| Published in: | Journal of Digital Imaging Vol. 35; no. 6; pp. 1662 - 1673 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160503235&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160503235 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2022 vid: 35 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160503235 160503235 160503235 10.1007/s10278-022-00654-3 160503235 ppf: 1662 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment. aug: au: Huang, Yu Moreno, Raquel Malani, Rachna Meng, Alicia Swinburne, Nathaniel Holodny, Andrei I. Choi, Ye Rusinek, Henry Golomb, James B. George, Ajax Parra, Lucas C. Young, Robert J. affil: Department of Radiology, Memorial Sloan Kettering Cancer Center, 10065, New York, NY, USA sug: subj: Hydrocephalus Diagnosis Hydrocephalus Surgery Magnetic Resonance Imaging Health Screening Deep Learning Neural Networks (Computer) Diagnosis, Computer Assisted Image Interpretation, Computer Assisted Radiologists Neuroradiography Human Retrospective Design Record Review Algorithms Automation Imaging, Three-Dimensional Image Processing, Computer Assisted Comparative Studies Clinical Assessment Tools Sensitivity and Specificity Descriptive Statistics ab: In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leveraged 496 clinical MRI exams collected retrospectively at a single clinical site from patients referred for any reason. This diagnostic dataset was enriched to have 259 hydrocephalus cases. A 3D convolutional neural network was trained on 16 manually segmented exams (ten hydrocephalus) and subsequently used to automatically segment the remaining 480 exams and extract volumetric anatomical features. A linear classifier of these features was trained on 240 exams to detect cases of hydrocephalus that required treatment with surgical intervention. Performance was compared to four neuroradiologists on the remaining 240 exams. Performance was also evaluated on a separate screening dataset of 451 exams collected from a routine clinical population to predict the consensus reading from four neuroradiologists using images alone. The pipeline was also tested on an external dataset of 31 exams from a 2nd clinical site. The most discriminant features were the Magnetic Resonance Hydrocephalic Index (MRHI), ventricle volume, and the ratio between ventricle and brain volume. At matching sensitivity, the specificity of the machine and the neuroradiologists did not show significant differences for detection of hydrocephalus on either dataset (proportions test, p > 0.05). ROC performance compared favorably with the state-of-the-art (AUC 0.90–0.96), and replicated in the external validation. Hydrocephalus cases requiring treatment can be detected automatically from MRI in a heterogeneous patient population based on quantitative characterization of brain anatomy with performance comparable to that of neuroradiologists. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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