HomeTmall Unveils MAOSS AI Model for Fatty Liver Disease Screening
Alibaba DAMO Academy has announced the joint development of an AI model for fatty liver screening, named MAOSS, in collaboration with several institutions including Shengjing Hospital of China Medical University and Nanjing University's Gulou Hospital. The study's findings were published in the prestigious international journal, Nature Communications, in February of this year.
Fatty liver disease affects over 30% of the population. Early symptoms are often mild and easily overlooked, allowing the condition to progress to liver fibrosis or cirrhosis. Traditional screening methods like B-ultrasound have limited sensitivity, while more specialized examinations are expensive. This frequently results in missed diagnoses for high-risk patients in clinical settings.
Key breakthroughs and advantages of the MAOSS model:
Deep analysis of non-contrast CT scans: DAMO Academy employed "non-contrast CT + AI" technology, enabling the AI to automatically extract high-dimensional features such as liver texture and density. This marks the first time liver steatosis and fibrosis staging can be assessed simultaneously using only a standard non-contrast CT scan.
Diagnostic accuracy exceeding physicians: In multi-center validation trials, the MAOSS model achieved an area under the curve (AUC) of 0.904-0.917 for liver steatosis staging, significantly higher than the average radiologist score of 0.709.
Doubled detection rate for high-risk cases: For the critical window to prevent cirrhosis (stage 2 fibrosis), the model identified 52.4% of high-risk patients, compared to a mere 16.6% detected through traditional clinical pathways—more than doubling the detection rate.
Early warning for cirrhosis risk: Follow-up data indicates that patients flagged as high-risk by MAOSS had a 45.5% probability of developing cirrhosis within two years, a rate far exceeding that of the low-risk group.
Experts from DAMO Academy noted that the model can leverage existing non-contrast CT data from routine physical exams and outpatient visits. This facilitates "front-end prevention" in chronic liver disease management without incurring additional patient costs. In the future, primary care hospitals could use this AI technology to provide high-risk warnings during standard health screenings, enabling earlier detection and potential reversal of the condition.
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Alibaba DAMO Academy has announced the joint development of an AI model for fatty liver screening, named MAOSS, in collaboration with several institutions including Shengjing Hospital of China Medical University and Nanjing University's Gulou Hospital. The study's findings were published in the prestigious international journal, Nature Communications, in February of this year.
Fatty liver disease affects over 30% of the population. Early symptoms are often mild and easily overlooked, allowing the condition to progress to liver fibrosis or cirrhosis. Traditional screening methods like B-ultrasound have limited sensitivity, while more specialized examinations are expensive. This frequently results in missed diagnoses for high-risk patients in clinical settings.
Key breakthroughs and advantages of the MAOSS model:
Deep analysis of non-contrast CT scans: DAMO Academy employed "non-contrast CT + AI" technology, enabling the AI to automatically extract high-dimensional features such as liver texture and density. This marks the first time liver steatosis and fibrosis staging can be assessed simultaneously using only a standard non-contrast CT scan.
Diagnostic accuracy exceeding physicians: In multi-center validation trials, the MAOSS model achieved an area under the curve (AUC) of 0.904-0.917 for liver steatosis staging, significantly higher than the average radiologist score of 0.709.
Doubled detection rate for high-risk cases: For the critical window to prevent cirrhosis (stage 2 fibrosis), the model identified 52.4% of high-risk patients, compared to a mere 16.6% detected through traditional clinical pathways—more than doubling the detection rate.
Early warning for cirrhosis risk: Follow-up data indicates that patients flagged as high-risk by MAOSS had a 45.5% probability of developing cirrhosis within two years, a rate far exceeding that of the low-risk group.
Experts from DAMO Academy noted that the model can leverage existing non-contrast CT data from routine physical exams and outpatient visits. This facilitates "front-end prevention" in chronic liver disease management without incurring additional patient costs. In the future, primary care hospitals could use this AI technology to provide high-risk warnings during standard health screenings, enabling earlier detection and potential reversal of the condition.
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