Scholarlink

<p>Predicting remission in Cushing’s disease (CD) following transsphenoidal surgery (TSS) is crucial for improving treatment strategies and patient outcomes. This systematic review and meta-analysis aimed to evaluate the application of Artificial Intelligence (AI) algorithms in forecasting remission outcomes, integrating diverse clinical, biochemical, and imaging data to enhance predictive accuracy. A comprehensive search was conducted across PubMed, Web of Science, Embase, Scopus, and Google Scholar databases up to December 2024, adhering to PRISMA guidelines. Out of 1,571 records identified, five studies involving 1,938 patients met the inclusion criteria. The pooled sensitivity and specificity of Machine learning (ML) models were 0.93 [95% CI: 0.65–0.99] and 0.78 [95% CI: 0.50–0.93], respectively, with the diagnostic score was 3.8 [95% CI: 0.86–6.74] and diagnostic odds ratio (DOR) of 44.79 [95% CI: 2.37–846.66]. The positive diagnostic likelihood ratio (DLR) was 4.23 [95% CI: 1.39–12.86], while the negative DLR was 0.09 [95% CI: 0.01–0.67]. The area under the curve (AUC) was 0.91. These results underscore the significant potential of AI algorithms in enhancing clinical decision-making and improving the prediction of remission in CD. However, methodological heterogeneity and the lack of external validation across studies call for standardized approaches to ensure broader applicability and reliability of these models in clinical settings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prediction of remission in cushing’s disease using artificial intelligence: A systematic review and meta-analysis

  • 期刊论文

  • 发表日期：

    2026年2月5日

查看全文
  • Mohammadamin Sabbagh Alvani,
  • Ibrahim Mohammadzadeh,
  • Bardia Hajikarimloo,
  • Sai Sanikommu,
  • Ali Mortezaei,
  • Pooya Eini,
  • Shahin Mohammadzadeh,
  • Mohammad Amin Habibi,
  • Seyed Ali Mousavinejad,
  • Vratko Himic,
  • Antonio Di Ieva,
  • Ricardo J. Komotar

摘要

Predicting remission in Cushing’s disease (CD) following transsphenoidal surgery (TSS) is crucial for improving treatment strategies and patient outcomes. This systematic review and meta-analysis aimed to evaluate the application of Artificial Intelligence (AI) algorithms in forecasting remission outcomes, integrating diverse clinical, biochemical, and imaging data to enhance predictive accuracy. A comprehensive search was conducted across PubMed, Web of Science, Embase, Scopus, and Google Scholar databases up to December 2024, adhering to PRISMA guidelines. Out of 1,571 records identified, five studies involving 1,938 patients met the inclusion criteria. The pooled sensitivity and specificity of Machine learning (ML) models were 0.93 [95% CI: 0.65–0.99] and 0.78 [95% CI: 0.50–0.93], respectively, with the diagnostic score was 3.8 [95% CI: 0.86–6.74] and diagnostic odds ratio (DOR) of 44.79 [95% CI: 2.37–846.66]. The positive diagnostic likelihood ratio (DLR) was 4.23 [95% CI: 1.39–12.86], while the negative DLR was 0.09 [95% CI: 0.01–0.67]. The area under the curve (AUC) was 0.91. These results underscore the significant potential of AI algorithms in enhancing clinical decision-making and improving the prediction of remission in CD. However, methodological heterogeneity and the lack of external validation across studies call for standardized approaches to ensure broader applicability and reliability of these models in clinical settings.