Accurate assessment of nonalcoholic fatty liver disease lesions in liver allograft biopsies by a smartphone platform: A proof of concept.

Macrovesicular steatosis (MS) is a major risk factor for liver graft failure after transplantation and pathological microscopic examination of a frozen tissue section remains the gold standard for its assessment. However, the latter requires an experienced in-house pathologist for correct and rapid diagnosis as well as specific equipment that is not always available. Smartphones, which are must-have tools for everyone, are very suitable for incorporation into promising technology to generate moveable diagnostic tools as for telepathology. The study aims to compare the microscopic assessment of nonalcoholic fatty liver disease (NAFLD) spectrum in liver allograft biopsies by a smartphone microscopy platform (DIPLE device) to standard light microscopy. Forty-two liver graft biopsies were evaluated in transmitted light, using an iPhone X and the microscopy platform. A significant correlation was reported between the two different approaches for graft MS assessment (Spearman's correlation coefficient: r = .93; p < .001) and for steatohepatitis feature (r = .56; p < .001; r = .45; p < .001). Based on these findings, a smartphone integrated with a cheap microscopy platform can achieve adequate accuracy in the assessment of NAFLD in liver graft and could be used as an alternative to standard light microscopy when the latter is unavailable.

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

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.

Assessment of Liver Graft Steatosis: Where Do We Stand?

The growing number of patients on waiting lists for liver transplantation and the shortage of organs have forced many centers to adopt extended criteria for graft selection, moving the limit of acceptance for marginal livers. Steatotic grafts that were, in the past, considered strictly unacceptable for transplantation because of the high risk of early nonfunction are now considered as a potential resource for organ implementation. Several methods to diagnose, measure, classify, and stage steatosis exist, but none can be considered qualitatively and quantitatively "the ideal method" to date. Clinical, biological, and imaging data can be very helpful to estimate graft steatosis, but histology still remains the gold standard. There is an increasing need for rapid and reliable tools to assess graft steatosis. Herein, we present a comprehensive review of the approaches that are currently used to quantify steatosis in liver grafts.

Computer-assisted liver graft steatosis assessment via learning-based texture analysis.

PURPOSE: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. METHODS: Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was [Formula: see text]. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ([Formula: see text]), and gray-level co-occurrence matrix ([Formula: see text]) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. RESULTS: With the best-performing feature set ([Formula: see text]) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. CONCLUSIONS: This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR.

Testing feasibility of an accurate microscopic assessment of macrovesicular steatosis in liver allograft biopsies by smartphone add-on lenses.

Light microscopy is an essential tool in histological examination of tissue samples. However, the required equipment for a correct and rapid diagnosis is sometimes unavailable. Smartphones and mobile phone networks are widespread, and could be used for diagnostic imaging and telemedicine. Macrovesicular steatosis (MS) is a major risk factor for liver graft failure, and is only assessable by microscopic examination of a frozen tissue section. The aim of this study was to compare the microscopic assessment of MS in liver allograft biopsies by a smartphone with eyepiece adaptor (BLIPS device) to standard light microscopy. Forty liver graft biopsies were evaluated in transmitted light, using an Iphone 5s and 4 different mini-objective, add-on lenses. A significant correlation was reported between the two different approaches for graft MS assessment (Spearman's correlation coefficient: rs = 0.946; p < .001). Smartphone with eyepiece adaptor had similar discriminatory power to identify MS in liver grafts than standard light microscopy. Based on these findings, a smartphone integrated with a low-cost eyepiece adaptor can achieve adequate accuracy in the assessment of MS in liver graft, and could be used as an alternative to standard light microscope when unavailable.

The treatment of breast cancer in one day surgery. A four year experience.

The number of short-stay surgery procedures has progressively increased since the concept of short-stay surgery was first introduced. Initially this type of surgery was reserved for patients undergoing inguinal hernia repair, proctological surgery, and various minor procedures. Careful patient selection makes it possible to apply one-day surgery to other surgical specialties including breast cancer surgery. Reducing the length of hospital stay lowers health care costs, and shortens waiting lists. The most important benefits for patients are a more rapid return to work and positive psychological effects. Exclusion criteria for one-day surgery are the lack of home care, excessive distance from place of treatment and the presence of any concomitant pathology that is a contraindication to this type of surgery. We report our experience in oncological surgery of the breast in one-day surgery.