Comparison of CT and clinical findings of Terson's syndrome in 121 patients: a 1-year prospective study

Koskela, Elina; Pekkola, Johanna; Kivisaari, Riku; Kivelä, Tero; Hernesniemi, Juha; Setälä, Kai; Laakso, Aki

doi:10.3171/2014.2.jns131248

Cited by 17 publications

(22 citation statements)

References 34 publications

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“…We observed ocular alterations on CT in patients with VH and/or PH, but not in individuals with RH. Furthermore, results are in line with data published by Koselka et al, recently . They found a sensitivity of the CT findings for any OH of 42% and a specificity of 97%.…”

Section: Discussionsupporting

confidence: 92%

“…Furthermore, results are in line with data published by Koselka et al, recently. 17 They found a sensitivity of the CT findings for any OH of 42% and a specificity of 97%. Moreover, intraobserver agreement was good with a κ of 0.69.…”

Section: Discussionmentioning

confidence: 49%

“…In order to refer these patients to ophthalmologic follow‐up and therapy a thorough funduscopic examination is recommended in SAH and remains the gold standard in the diagnosis of OH . Two studies also found head computed tomography (CT) to be valuable for diagnosing Terson's syndrome in patients with SAH . In the acute therapy of SAH, however, OH are easily underdiagnosed and ophthalmologists are not consulted routinely .…”

Section: Introductionmentioning

confidence: 99%

“…9 Two studies also found head computed tomography (CT) to be valuable for diagnosing Terson's syndrome in patients with SAH. 17,18 In the acute therapy of SAH, however, OH are easily underdiagnosed and ophthalmologists are not consulted routinely. 9,16 Additionally, funduscopy is performed infrequently in acute care settings and is also not accurately interpreted by nonophthalmologists.…”

Section: Introductionmentioning

confidence: 99%

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Terson's Syndrome: Diagnostic Comparison of Ocular Sonography and CT

Bäuerle

Gross

Egger

et al. 2015

Journal of Neuroimaging

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 49%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Terson's Syndrome: Diagnostic Comparison of Ocular Sonography and CT

Bäuerle

Gross

Egger

et al. 2015

Journal of Neuroimaging

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“…The sudden increase in intracranial pressure may cause an acute hemorrhage into the retina and vitreous of the eye with sudden loss of vision, which is referred to as Terson's syndrome. 29 The hemorrhages may be demonstrated on CT, but are easily seen on funduscopic exam. Vision loss may also occur from rupture of an ophthalmic artery aneurysm or from vasospasm-related infarction that may occur after aneurysmal rupture.…”

Section: Vascularmentioning

confidence: 98%

Headache and the Eye

Frishberg

2015

Headache and Migraine Biology and Management

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Development of a Deep Learning Model for Retinal Hemorrhage Detection on Head Computed Tomography in Young Children

Gunturkun,

Bakir-Batu,

Siddiqui

et al. 2023

JAMA Netw Open

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ImportanceAbusive head trauma (AHT) in children is often missed in medical encounters, and retinal hemorrhage (RH) is considered strong evidence for AHT. Although head computed tomography (CT) is obtained routinely, all but exceptionally large RHs are undetectable on CT images in children.ObjectiveTo examine whether deep learning–based image analysis can detect RH on pediatric head CT.Design, Setting, and ParticipantsThis diagnostic study included 301 patients diagnosed with AHT who underwent head CT and dilated fundoscopic examinations at a quaternary care children’s hospital. The study assessed a deep learning model using axial slices from 218 segmented globes with RH and 384 globes without RH between May 1, 2007, and March 31, 2021. Two additional light gradient boosting machine (GBM) models were assessed: one that used demographic characteristics and common brain findings in AHT and another that combined the deep learning model’s risk prediction plus the same demographic characteristics and brain findings.Main Outcomes and MeasuresSensitivity (recall), specificity, precision, accuracy, F1 score, and area under the curve (AUC) for each model predicting the presence or absence of RH in globes were assessed. Globe regions that influenced the deep learning model predictions were visualized in saliency maps. The contributions of demographic and standard CT features were assessed by Shapley additive explanation.ResultsThe final study population included 301 patients (187 [62.1%] male; median [range] age, 4.6 [0.1-35.8] months). A total of 120 patients (39.9%) had RH on fundoscopic examinations. The deep learning model performed as follows: sensitivity, 79.6%; specificity, 79.2%; positive predictive value (precision), 68.6%; negative predictive value, 87.1%; accuracy, 79.3%; F1 score, 73.7%; and AUC, 0.83 (95% CI, 0.75-0.91). The AUCs were 0.80 (95% CI, 0.69-0.91) for the general light GBM model and 0.86 (95% CI, 0.79-0.93) for the combined light GBM model. Sensitivities of all models were similar, whereas the specificities of the deep learning and combined light GBM models were higher than those of the light GBM model.Conclusions and RelevanceThe findings of this diagnostic study indicate that a deep learning–based image analysis of globes on pediatric head CTs can predict the presence of RH. After prospective external validation, a deep learning model incorporated into CT image analysis software could calibrate clinical suspicion for AHT and provide decision support for which patients urgently need fundoscopic examinations.

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Comparison of CT and clinical findings of Terson's syndrome in 121 patients: a 1-year prospective study

Abstract: Routinely looking for TS findings in CT head scans may prove valuable in clinical practice. Terson's syndrome is associated with female sex and poor clinical condition on admission.

Cited by 17 publications

References 34 publications

Terson's Syndrome: Diagnostic Comparison of Ocular Sonography and CT

Terson's Syndrome: Diagnostic Comparison of Ocular Sonography and CT

Headache and the Eye

Development of a Deep Learning Model for Retinal Hemorrhage Detection on Head Computed Tomography in Young Children

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