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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.
The AIG-SLD Screening Project is a single-arm, open-label, prospective interventional study designed to evaluate the effectiveness of a MAOSS-guided identification and AI-human collaboration recall strategy for detecting individuals at intermediate or high risk of steatotic liver disease (SLD) and for assessing intervention outcomes. The trail will prospectively and consecutively enroll around 8000 eligible adults aged ≥18 years who undergo routine chest or abdominal non-contrast CT (NCCT) with adequate hepatic coverage .
The AI system i.e. MAOSS will be embedded within the standard clinical workflow to evaluate the real-world performance and the impact of AIG-SLD screening. All eligible NCCT scans will be evaluated through two parallel streams:
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.
Primary hepatology care begins with the collection of questionnaires regarding medical history, lifestyle, alcohol consumption, and metabolic risks (Type 2 Diabetes Mellitus, obesity, metabolic syndrome, significant alcohol consumption, or viral hepatitis) followed by further serum laboratory tests and Transient Elastography (e.g., FibroScan). Recalled patients will be managed according to the MAOSS pathway starting with FIB-4 stratification (<1.3, 1.3-2.67, >2.67), followed by FAST stratification (<0.35, ≥0.35) as needed. Intermediate-to-high-risk patients will proceed to escalated care involving MRE, MRI-PDFF, or liver biopsy. Management is then determined by MRE-derived Liver Stiffness Measurement (LSM): patients with LSM < 3.5 kPa will receive lifestyle interventions and annual reassessment; those with LSM 3.5-5.0 kPa (F2-F3) will receive pharmacological or therapeutic interventions; and those with LSM ≥ 5.0 kPa (F4) will undergo cirrhosis-based management. Patients in the latter two groups will be reassessed every six months to monitor changes in steatosis and fibrosis.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-human collaboration for SLD screening | Experimental | In the prospective analysis phase, all eligible NCCT scans will be evaluated through two parallel streams: 1. Standard of Care (SoC) workflow: Radiologists perform independent assessments as per standard clinical procedures (e.g., first-line radiologists' reviews followed by senior radiologist finalizing the report). 2. AIG workflow: The MAOSS system simultaneously analyzes the identical imaging data in real-time. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-human collaboration for SLD screening | Diagnostic Test | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Effective referral yield for escalated hepatology care | 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. | 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Real-World Screening Specificity | A critical safety endpoint is to determine if AIG-SLD care pathway maintains a high specificity to prevent false referral, thereby avoiding unnecessary interventions. | Duration of the study (12 months) |
| Real-World Screening Sensitivity |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Shengjing Hospital of China Medical University | Shenyang | Liaoning | 110004 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41672973 | Background | Gao Y, Li C, Chang W, Du B, Ye X, Yeo YH, Xia Y, Guo H, Zhang X, Liu W, Bai R, Li B, Hong Y, Yao J, Lu L, Cao K, Yan K, Chen J, Li J, Hou Y, Zhang L, Shi Y. Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease. Nat Commun. 2026 Feb 11;17(1):1562. doi: 10.1038/s41467-026-68414-3. |
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A critical efficacy endpoint is to determine if AIG-SLD care pathway is sufficiently sensitive to identify patients who actually need interventions. |
| Duration of the study (12 months) |
| Real-World Screening Negative predictive value | This evaluates the AIG-SLD care pathway's ability to correctly exclude low-risk patients who actually do not need interventions. | Duration of the study (12 months) |
| Intervention Success (Fibrosis Reversion) | The proportion of patients referred for escalated care achieves fibrosis stage reversion. | Duration of the study (12 months) |
| Intervention Success (Steatosis Reversion) | The proportion of patients referred for escalated care achieves steatosis stage Reversion. | Duration of the study (12 months) |
| Quantitative Liver Stiffness Trends | The overall longitudinal changes in Liver Stiffness Measurement (LSM) values for all patients referred to escalated care. | Duration of the study (12 months) |
| Quantitative Liver Steatosis Trends | The overall longitudinal changes in Liver Fat Content (CAP/PDFF) values for all patients referred to escalated care. | Duration of the study (12 months) |
| Intervention Patient Adherence | The overall intervention adherence rate for all patients referred to escalated care. | Duration of the study (12 months) |
| ID | Term |
|---|---|
| D005234 | Fatty Liver |
| D008103 | Liver Cirrhosis |
| ID | Term |
|---|---|
| D008107 | Liver Diseases |
| D004066 | Digestive System Diseases |
| D005355 | Fibrosis |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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