Use of Artificial Intelligence as an Innovative Method for Liver Graft Macrosteatosis Assessment

Abstract

The worldwide implementation of a liver graft pool using marginal livers (ie, grafts with a high risk of technical complications and impaired function or with a risk of transmitting infection or malignancy to the recipient) has led to a growing interest in developing methods for accurate evaluation of graft quality. Liver steatosis is associated with a higher risk of primary nonfunction, early graft dysfunction, and poor graft survival rate. The present study aimed to analyze the value of artificial intelligence (AI) in the assessment of liver steatosis during procurement compared with liver biopsy evaluation. A total of 117 consecutive liver grafts from brain‐dead donors were included and classified into 2 cohorts: ≥30 versus <30% hepatic steatosis. AI analysis required the presence of an intraoperative smartphone liver picture as well as a graft biopsy and donor data. First, a new algorithm arising from current visual recognition methods was developed, trained, and validated to obtain automatic liver graft segmentation from smartphone images. Second, a fully automated texture analysis and classification of the liver graft was performed by machine‐learning algorithms. Automatic liver graft segmentation from smartphone images achieved an accuracy (Acc) of 98%, whereas the analysis of the liver graft features (cropped picture and donor data) showed an Acc of 89% in graft classification (≥30 versus <30%). This study demonstrates that AI has the potential to assess steatosis in a handy and noninvasive way to reliably identify potential nontransplantable liver grafts and to avoid improper graft utilization.

Pilot study of a noninvasive real‐time optical backscatter probe in liver transplantation

“…Fibroscan, CT and MRI have also been used successfully as a noninvasive tools for quantifying fat in the field of nonalcoholic fatty liver disease [37], but their role may be limited in the setting of liver transplantation by their portability, availability 24 hours per day across all potential donor hospitals, national laws about pre‐mortem interventions in donors and cost. Other groups have also demonstrated the effectiveness of analysis of smartphone photographs and digital analysis software to assess the extent of macrovesicular steatosis [38,39]. Novel biomarkers for fatty liver disease are also currently being investigated [40], but would need to be validated in the context of organ donors.…”

“…Increased backscatter readings subsequently correlated with increased MEAF scores (Pearson's r = 0.33 (95% CI 0.14 to 0.49), R 2 = 0.11, P = 0.0011) (c); this was particularly true in organs from DCD donors (r = 0.77 (95% CI 0.50 to 0.90), R 2 = 0.59, P < 0.0001) compared to DBD (r = 0.23 (95% CI 0.0019 to 0.43), R 2 = 0.52, P = 0.048). steatosis [38,39]. Novel biomarkers for fatty liver disease are also currently being investigated [40], but would need to be validated in the context of organ donors.…”

Artificial intelligence in precision medicine in hepatology

“…The input of novel data format from new sources, for example, the wearable devices and smart phones. A few studies are using smart phones to collect clinical images to aid diagnosis, such as neonatal jaundice 79 and hepatic steatosis 80 . The identification of new therapeutic targets via synthesis of molecular, genetic, and clinical data from large patient datasets is practicable 10 .…”

Predicting Early Allograft Dysfunction after Liver Transplantation from Post-Reperfusion Donor Liver Image

“…Notably, TA has transformed the subjective visual evaluation of liver texture into a quantitative and objective method [6] . Sara et al [7] performed TA of liver RGB image using machine learning algorithms, and eventually developed an arti cial intelligence for evaluation of graft hepatic steatosis [6] .…”

“…Notably, TA has transformed the subjective visual evaluation of liver texture into a quantitative and objective method [6] . Sara et al [7] performed TA of liver RGB image using machine learning algorithms, and eventually developed an arti cial intelligence for evaluation of graft hepatic steatosis [6] . With the ubiquity of smartphones, there is a colossal utilization of TA of RGB images for diagnosis of diseases or classi cation of tissues in other medical elds, including the diagnosis of skin cancer [8] .…”