Response to: Precision in Diagnosis of Liver Fibrosis in MASLD: Machine Learning Based Scores May Be More Accurate Than Conventional NITs DOI Open Access
Yasaman Vali, Quentin M. Anstee, Patrick M. Bossuyt

и другие.

Liver International, Год журнала: 2025, Номер 45(5)

Опубликована: Март 29, 2025

We thank Soliman et al. for their thoughtful comments on our recent publication [1]. appreciate insights and the opportunity to further discuss role of machine learning (ML)-based non-invasive tests (NITs) in diagnosis metabolic dysfunction-associated steatotic liver disease (MASLD). As we previously demonstrated LITMUS study, ML models incorporating clinical biomarker data can enhance detection nonalcoholic steatohepatitis (NASH) at-risk NASH, highlighting potential ML-based approaches diagnostics [2]. Expanding fibrosis assessment, highlight scores, particularly FIB-6 index, improving accuracy assessment MASLD [3]. Their multicenter study that score, which integrates multiple routine laboratory parameters, could offer advantages sensitivity negative predictive value (NPV) compared conventional NITs like FIB-4 or APRI [4]. also utility resource-limited settings, where advanced imaging techniques transient elastography may not be readily available. However, generalisability these scores across diverse populations settings remains fully validated. While has been evaluated cohorts patients with chronic hepatitis C, B, MASLD, studies are needed assess its performance primary care comorbidities beyond those studied. In addition, while such as hold promise refining diagnostic accuracy, implementation requires careful consideration. Our emphasised importance tailoring NIT thresholds individual patient characteristics, age, body mass index (BMI), diabetes status, optimise accuracy. This approach aligns principles personalised medicine enhanced by models. acknowledged findings primarily reflect secondary tertiary need research populations. Moreover, leveraged rigorous histological assessments from centres, variability biopsy interpretation a recognised limitation. The upcoming results cohort, using centralised AI-based pathology, will provide into standardised evaluation. conclusion, represent promising advancement diagnosis, integration practice should guided validation consideration patient-specific factors. look forward future explore other diseases. authors declare no conflicts interest.

Язык: Английский

Response to: Precision in Diagnosis of Liver Fibrosis in MASLD: Machine Learning Based Scores May Be More Accurate Than Conventional NITs DOI Open Access
Yasaman Vali, Quentin M. Anstee, Patrick M. Bossuyt

и другие.

Liver International, Год журнала: 2025, Номер 45(5)

Опубликована: Март 29, 2025

We thank Soliman et al. for their thoughtful comments on our recent publication [1]. appreciate insights and the opportunity to further discuss role of machine learning (ML)-based non-invasive tests (NITs) in diagnosis metabolic dysfunction-associated steatotic liver disease (MASLD). As we previously demonstrated LITMUS study, ML models incorporating clinical biomarker data can enhance detection nonalcoholic steatohepatitis (NASH) at-risk NASH, highlighting potential ML-based approaches diagnostics [2]. Expanding fibrosis assessment, highlight scores, particularly FIB-6 index, improving accuracy assessment MASLD [3]. Their multicenter study that score, which integrates multiple routine laboratory parameters, could offer advantages sensitivity negative predictive value (NPV) compared conventional NITs like FIB-4 or APRI [4]. also utility resource-limited settings, where advanced imaging techniques transient elastography may not be readily available. However, generalisability these scores across diverse populations settings remains fully validated. While has been evaluated cohorts patients with chronic hepatitis C, B, MASLD, studies are needed assess its performance primary care comorbidities beyond those studied. In addition, while such as hold promise refining diagnostic accuracy, implementation requires careful consideration. Our emphasised importance tailoring NIT thresholds individual patient characteristics, age, body mass index (BMI), diabetes status, optimise accuracy. This approach aligns principles personalised medicine enhanced by models. acknowledged findings primarily reflect secondary tertiary need research populations. Moreover, leveraged rigorous histological assessments from centres, variability biopsy interpretation a recognised limitation. The upcoming results cohort, using centralised AI-based pathology, will provide into standardised evaluation. conclusion, represent promising advancement diagnosis, integration practice should guided validation consideration patient-specific factors. look forward future explore other diseases. authors declare no conflicts interest.

Язык: Английский

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