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Objective <p>Obstructive hydrocephalus is a critical radiographic finding requiring emergent treatment. Its identification on head CT by an AI model could facilitate sooner life-saving interventions, although there are common co-occurring findings, including intracranial hemorrhage, that can confound this interpretation. This external validation assessed the accuracy of an AI model at identifying obstructive hydrocephalus, including in the presence or absence of other findings.</p> Materials and methods <p>This retrospective cohort included 177 thin (≤ 1.5 mm) series and 194 thick (&gt; 1.5 and ≤ 5 mm) series from 200 non-contrast head CT cases. These cases were obtained from patients aged ≥ 18 years at 5 hospitals in the United States. Each case was interpreted independently by up to three neuroradiologists. Each series was then interpreted by the AI model.</p> Results <p>The AI model performed with an area under the curve of 0.988 (95% confidence interval (CI): 0.971–0.998) on thin series and 0.986 (95% CI: 0.969–0.997) on thick series. These results were broadly maintained in subgroups for the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain, and across demographic and scanner manufacturer subgroups.</p> Conclusions <p>The AI model accurately identified obstructive hydrocephalus in this dataset. Its performance in subgroup analyses reflected its robustness.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can an artificial intelligence model accurately identify obstructive hydrocephalus on head computed tomography, including in the presence or absence of common co-occurring imaging findings?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>This model accurately identified obstructive hydrocephalus on thin and thick series, including in the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>This model could assist with triaging abnormal cases, enabling earlier identification and management of obstructive hydrocephalus. Its maintained performance with or without co-occurring findings suggests it specifically identifies obstructive hydrocephalus rather than these findings</i>.</p> Graphical Abstract <p></p>

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Evaluation of an artificial intelligence model for the identification of obstructive hydrocephalus on computed tomography of the head

  • 期刊论文

  • 发表日期：

    2026年2月4日

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  • Ankita Ghatak,
  • Isabella Newbury-Chaet,
  • Sarah F. Mercaldo,
  • John K. Chin,
  • Madeleine A. Halle,
  • Eric L’Italien,
  • Ashley L. MacDonald,
  • Alex S. Schultz,
  • Karen Buch,
  • John Conklin,
  • William A. Mehan,
  • Stuart Pomerantz,
  • Sandra Rincon,
  • Bernardo C. Bizzo,
  • James M. Hillis

摘要

Objective

Obstructive hydrocephalus is a critical radiographic finding requiring emergent treatment. Its identification on head CT by an AI model could facilitate sooner life-saving interventions, although there are common co-occurring findings, including intracranial hemorrhage, that can confound this interpretation. This external validation assessed the accuracy of an AI model at identifying obstructive hydrocephalus, including in the presence or absence of other findings.

Materials and methods

This retrospective cohort included 177 thin (≤ 1.5 mm) series and 194 thick (> 1.5 and ≤ 5 mm) series from 200 non-contrast head CT cases. These cases were obtained from patients aged ≥ 18 years at 5 hospitals in the United States. Each case was interpreted independently by up to three neuroradiologists. Each series was then interpreted by the AI model.

Results

The AI model performed with an area under the curve of 0.988 (95% confidence interval (CI): 0.971–0.998) on thin series and 0.986 (95% CI: 0.969–0.997) on thick series. These results were broadly maintained in subgroups for the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain, and across demographic and scanner manufacturer subgroups.

Conclusions

The AI model accurately identified obstructive hydrocephalus in this dataset. Its performance in subgroup analyses reflected its robustness.

Key Points

Question Can an artificial intelligence model accurately identify obstructive hydrocephalus on head computed tomography, including in the presence or absence of common co-occurring imaging findings?

Findings This model accurately identified obstructive hydrocephalus on thin and thick series, including in the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain.

Clinical relevance This model could assist with triaging abnormal cases, enabling earlier identification and management of obstructive hydrocephalus. Its maintained performance with or without co-occurring findings suggests it specifically identifies obstructive hydrocephalus rather than these findings.

Graphical Abstract