Interventional AI-Human Collaboration for Steatotic Liver Disease Screening

Condition(s) studied

Investigational drug(s) / intervention(s)

AI-human collaboration for SLD screening: The system screens patients with clinically suspected SLD by flagging those with a MAOSS score ≥1.6 and a FIBRO Score ≥1.7 for recall. These algorithmic flags will be compared against radiologists' determinations of clinically significant SLD. Management pathways are defined as follows: (1) Concordant cases: If the Standard of Care (SoC) and the AIG pathway agree (both recommending recall or both recommending no recall), the agreed-upon decision will be executed. (2) Discordant cases: If the SoC and AIG pathways disagree, patients will be recalled for primary hepatology care to ensure safety and avoid potential missed diagnosis.

Study summary

Steatotic liver disease (SLD) is one of the most prevalent chronic liver diseases worldwide, affecting nearly 30% of the global population and projected to exceed 55% by 2040. Timely identification and management of intermediate- and high-risk SLD patients are essential, yet early detection remains challenging because current diagnostic modalities, such as biopsy, ultrasonography, and serum indices, are invasive, insensitive, operator-dependent, or difficult to scale. In contrast, non-contrast CT is widely available in routine care and offers substantial potential for opportunistic SLD screening, although this value has not been fully utilized. Our previously developed MAOSS model accurately identifies intermediate- and high-risk individuals, with MAOSS score≥1.6 combined with Fibro Score ≥1.7, demonstrating high sensitivity and specificity in our large-scale retrospective study. However, despite these promising retrospective findings, the model has not undergone prospective interventional validation, and it remains unclear whether an AI-guided workflow can truly enhance clinical risk stratification, diagnostic yield, and downstream management in real-world SLD populations. Therefore, a prospective intervention study is needed to determine whether MAOSS-guided identification and recall of at-risk individuals can meaningfully improve fibrosis detection and optimize clinical care pathways for SLD.

Eligibility

Primary outcome measure(s)

• Effective referral yield for escalated hepatology care — 6 months
  Effective referral yield: the proportion of patients referred for escalated hepatology care confirmed with clinically significant (or above) fibrosis by MRE/liver biposy. A non-inferiority test (and estimates of the associated absolute and relative differences) will be performed for effective referral yield between SoC and AIG workflow.

Trial sites (1)

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