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<p>Current detection methods of <i>Demodex</i> mite density in facial erythema are semi-invasive or operator-dependent. We developed and evaluated a deep learning model (DemodexNet) for predicting <i>Demodex</i> mite density and assessed its impact on the diagnostic performance of dermatologists. This study included 1,124 patients with facial erythema who underwent <i>Demodex</i> mite density measurement at two referral hospitals between January 2016 and August 2023. DemodexNet achieved area under the receiver operating characteristic curve values of 0.823–0.865 in internal testing, with lower values observed in the external testing set. AI-assisted evaluation was associated with an increase in diagnostic accuracy among dermatologists from 63.7% to 70.6% (<i>P</i> &lt; .001). Less experienced dermatologists and those with higher trust in AI showed greater performance gains. The model recognized central facial regions and individual lesions characteristic of demodicosis. DemodexNet demonstrates promising performance in predicting <i>Demodex</i> mite density and significantly improves dermatologists’ diagnostic accuracy. As this proof-of-concept study was limited to Korean patients with Fitzpatrick skin types III-IV, validation in diverse populations is required before broader clinical application.</p>

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Artificial intelligence-assisted prediction of Demodex mite density in facial erythema

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Jemin Kim,
  • Yun Na Lee,
  • Jihee Boo,
  • Inrok Oh,
  • Changyoon Lee,
  • Joo Hee Lee,
  • Ye Seul Choi,
  • Hyun Kim,
  • Jung Im Na,
  • Jihee Kim,
  • Chang Ook Park

摘要

Current detection methods of Demodex mite density in facial erythema are semi-invasive or operator-dependent. We developed and evaluated a deep learning model (DemodexNet) for predicting Demodex mite density and assessed its impact on the diagnostic performance of dermatologists. This study included 1,124 patients with facial erythema who underwent Demodex mite density measurement at two referral hospitals between January 2016 and August 2023. DemodexNet achieved area under the receiver operating characteristic curve values of 0.823–0.865 in internal testing, with lower values observed in the external testing set. AI-assisted evaluation was associated with an increase in diagnostic accuracy among dermatologists from 63.7% to 70.6% (P < .001). Less experienced dermatologists and those with higher trust in AI showed greater performance gains. The model recognized central facial regions and individual lesions characteristic of demodicosis. DemodexNet demonstrates promising performance in predicting Demodex mite density and significantly improves dermatologists’ diagnostic accuracy. As this proof-of-concept study was limited to Korean patients with Fitzpatrick skin types III-IV, validation in diverse populations is required before broader clinical application.