Detection of Large-Droplet Macrovesicular Steatosis in Donor Livers Based on Segment-Anything Model.

Liver transplantation is an effective treatment for end-stage liver disease, acute liver failure, and primary hepatic malignancy. However, the limited availability of donor organs remains a challenge. Severe large-droplet fat (LDF) macrovesicular steatosis, characterized by cytoplasmic replacement with large fat vacuoles, can lead to liver transplant complications. Artificial intelligence models, such as segmentation and detection models, are being developed to detect LDF hepatocytes. The Segment-Anything Model, utilizing the DEtection TRansformer architecture, has the ability to segment objects without prior knowledge of size or shape. We investigated the Segment-Anything Model's potential to detect LDF hepatocytes in liver biopsies. Pathologist-annotated specimens were used to evaluate model performance. The model showed high sensitivity but compromised specificity due to similarities with other structures. Filtering algorithms were developed to improve specificity. Integration of the Segment-Anything Model with rule-based algorithms accurately detected LDF hepatocytes. Improved diagnosis and treatment of liver diseases can be achieved through advancements in artificial intelligence algorithms for liver histology analysis.

Artificial intelligence-aided steatosis assessment in donor livers according to the Banff consensus recommendations.

OBJECTIVES: Severe macrovesicular steatosis in donor livers is associated with primary graft dysfunction. The Banff Working Group on Liver Allograft Pathology has proposed recommendations for steatosis assessment of donor liver biopsy specimens with a consensus for defining "large droplet fat" (LDF) and a 3-step algorithmic approach. METHODS: We retrieved slides and initial pathology reports from potential liver donor biopsy specimens from 2010 to 2021. Following the Banff approach, we reevaluated LDF steatosis and employed a computer-assisted manual quantification protocol and artificial intelligence (AI) model for analysis. RESULTS: In a total of 113 slides from 88 donors, no to mild (<33%) macrovesicular steatosis was reported in 88.5% (100/113) of slides; 8.8% (10/113) was reported as at least moderate steatosis (≥33%) initially. Subsequent pathology evaluation, following the Banff recommendation, revealed that all slides had LDF below 33%, a finding confirmed through computer-assisted manual quantification and an AI model. Correlation coefficients between pathologist and computer-assisted manual quantification, between computer-assisted manual quantification and the AI model, and between the AI model and pathologist were 0.94, 0.88, and 0.81, respectively (P < .0001 for all). CONCLUSIONS: The 3-step approach proposed by the Banff Working Group on Liver Allograft Pathology may be followed when evaluating steatosis in donor livers. The AI model can provide a rapid and objective assessment of liver steatosis.

Improving Generalization of Deep Learning Models for Diagnostic Pathology by Increasing Variability in Training Data: Experiments on Osteosarcoma Subtypes.

BACKGROUND: Artificial intelligence has an emerging progress in diagnostic pathology. A large number of studies of applying deep learning models to histopathological images have been published in recent years. While many studies claim high accuracies, they may fall into the pitfalls of overfitting and lack of generalization due to the high variability of the histopathological images. AIMS AND OBJECTS: Use the model training of osteosarcoma as an example to illustrate the pitfalls of overfitting and how the addition of model input variability can help improve model performance. MATERIALS AND METHODS: We use the publicly available osteosarcoma dataset to retrain a previously published classification model for osteosarcoma. We partition the same set of images into the training and testing datasets differently than the original study: the test dataset consists of images from one patient while the training dataset consists images of all other patients. We also show the influence of training data variability on model performance by collecting a minimal dataset of 10 osteosarcoma subtypes as well as benign tissues and benign bone tumors of differentiation. RESULTS: The performance of the re-trained model on the test set using the new partition schema declines dramatically, indicating a lack of model generalization and overfitting. We show the additions of more and moresubtypes into the training data step by step under the same model schema yield a series of coherent models with increasing performances. CONCLUSIONS: In conclusion, we bring forward data preprocessing and collection tactics for histopathological images of high variability to avoid the pitfalls of overfitting and build deep learning models of higher generalization abilities.

Contact-free measurement of cardiac pulse based on the analysis of thermal imagery.

We have developed a novel method to measure human cardiac pulse at a distance. It is based on the information contained in the thermal signal emitted from major superficial vessels. This signal is acquired through a highly sensitive thermal imaging system. Temperature on the vessel is modulated by pulsative blood flow. To compute the frequency of modulation (pulse), we extract a line-based region along the vessel. Then, we apply fast Fourier transform (FFT) to individual points along this line of interest to capitalize on the pulse's thermal propagation effect. Finally, we use an adaptive estimation function on the average FFT outcome to quantify the pulse. We have carried out experiments on a data set of 34 subjects and compared the pulse computed from our thermal signal analysis method to concomitant ground-truth measurements obtained through a standard contact sensor (piezo-electric transducer). The performance of the new method ranges from 88.52% to 90.33% depending on the clarity of the vessel's thermal imprint. To the best of our knowledge, it is the first time that cardiac pulse has been measured several feet away from a subject with passive means.

Counting heartbeats at a distance.

In the present paper, we propose a new pulse measurement methodology based on thermal imaging (contact-free). The method capitalizes both on the thermal undulation produced by the traveling pulse as well as the periodic expansion of the compliant vessel wall. The paper reports experiments on 34 subjects, where it compares the performance of the new pulse measurement method to the one we reported previously. The measurements were ground-truthed through a piezo-electric sensor. Statistical analysis reveals that the new imaging methodology is more accurate and robust than the previous one. Its performance becomes nearly perfect, when the vessel is not obstructed by a thick fat deposit.

Harvesting the thermal cardiac pulse signal.

In the present paper, we propose a new pulse measurement methodology based on thermal imaging (contact-free). The method capitalizes both on the thermal undulation produced by the traveling pulse as well as the periodic expansion of the compliant vessel wall. The paper reports experiments on 34 subjects, where it compares the performance of the new pulse measurement method to the one we reported previously. The measurements were ground-truthed through a piezo-electric sensor. Statistical analysis reveals that the new imaging methodology is more accurate and robust than the previous one. Its performance becomes nearly perfect, when the vessel is not obstructed by a thick fat deposit.