“…After segmenting one reference image, the classifier and its data were saved and imported for analysis of subsequent images. This approach enabled exact and dependable measurement of adipose tissue in images of zebrafish abdominal cavities, verified by skilled histopathologists during training of the classifier 31 , 32 . Figure 2 ( A ) Schematic spatial distribution of visceral fat.…”
Section: Methodsmentioning
confidence: 94%
“…2 ) was performed on each scan image using the graphical analysis software Fiji—ImageJ 1.54f. (National Institutes of Health, Bethesda, MD, USA; available at: http://rsb.info.nih.gov/ij/index.html ) as previously described 31 , 32 . Trainable Weka Segmentation (TWS), a plugin available within Fiji (ImageJ), was utilized for this analysis.…”
Obesity is a complex chronic condition associated with multiple health risks, including visceral obesity, which is particularly detrimental. To gain insight into the mechanisms underlying obesity and its associated pathologies, a novel zebrafish model was established using an innovative high-fat diet (HFD). The primary goal was to induce visceral obesity in zebrafish and study the associated structural changes. To achieve this, a unique HFD consisting of 40% beef fat (HFD40) was developed and supplemented with magnesium aluminometasilicate to improve stability in a high humidity environment. Feeding regimens were initiated for both juvenile (starting at 2 weeks post-fertilization, lasting 18 weeks) and adult zebrafish (3 months post-fertilization, 8 weeks feeding duration). The innovative dietary approach successfully induced visceral obesity in both juvenile and adult zebrafish. This new model provides a valuable tool to study obesity-related pathologies, metabolic syndrome, and potential therapeutic interventions. Most importantly, the low-cost and easy-to-prepare composition of HFD40 was seamlessly incorporated into the water without the need for separation, was readily absorbed by the fish and induced rapid weight gain in the zebrafish population. In conclusion, this study presents a novel HFD40 composition enriched with a high beef fat concentration (40%), which represents a significant advance in the development of an experimental zebrafish model for the study of visceral obesity and associated metabolic changes.
“…After segmenting one reference image, the classifier and its data were saved and imported for analysis of subsequent images. This approach enabled exact and dependable measurement of adipose tissue in images of zebrafish abdominal cavities, verified by skilled histopathologists during training of the classifier 31 , 32 . Figure 2 ( A ) Schematic spatial distribution of visceral fat.…”
Section: Methodsmentioning
confidence: 94%
“…2 ) was performed on each scan image using the graphical analysis software Fiji—ImageJ 1.54f. (National Institutes of Health, Bethesda, MD, USA; available at: http://rsb.info.nih.gov/ij/index.html ) as previously described 31 , 32 . Trainable Weka Segmentation (TWS), a plugin available within Fiji (ImageJ), was utilized for this analysis.…”
Obesity is a complex chronic condition associated with multiple health risks, including visceral obesity, which is particularly detrimental. To gain insight into the mechanisms underlying obesity and its associated pathologies, a novel zebrafish model was established using an innovative high-fat diet (HFD). The primary goal was to induce visceral obesity in zebrafish and study the associated structural changes. To achieve this, a unique HFD consisting of 40% beef fat (HFD40) was developed and supplemented with magnesium aluminometasilicate to improve stability in a high humidity environment. Feeding regimens were initiated for both juvenile (starting at 2 weeks post-fertilization, lasting 18 weeks) and adult zebrafish (3 months post-fertilization, 8 weeks feeding duration). The innovative dietary approach successfully induced visceral obesity in both juvenile and adult zebrafish. This new model provides a valuable tool to study obesity-related pathologies, metabolic syndrome, and potential therapeutic interventions. Most importantly, the low-cost and easy-to-prepare composition of HFD40 was seamlessly incorporated into the water without the need for separation, was readily absorbed by the fish and induced rapid weight gain in the zebrafish population. In conclusion, this study presents a novel HFD40 composition enriched with a high beef fat concentration (40%), which represents a significant advance in the development of an experimental zebrafish model for the study of visceral obesity and associated metabolic changes.
“…Finally, AI-pathology models have reported diagnostic potential with a model differentiating amyloid-containing biopsies from controls with 100% accuracy, improving inter-observer variability [79]. Moreover, some models could detect pre-symptomatic amyloidosis in ligamentum flavum, while others could predict the toxicity of light chains in AL amyloidosis with reasonable accuracy [82]. In summary, these AI models may change the landscape of CA's diagnostic evaluation and surveillance, not only by facilitating earlier detection but also by reducing work-up expenses and the need for invasive diagnostic modalities [9,89,90].…”
Section: Discussion and Clinical Implicationsmentioning
confidence: 99%
“…Wang et al investigated the ability of the ML approach to detect and classify amyloid depositions in histological slides of the ligamentum flavum preceding the progression to systemic amyloidosis [82]. This model was comparable to the gold standard of manual segmentation either in the training (R = 0.98; p = 0.0033) or the application set of histological images (R = 0.94; p = 0.016) [82].…”
Section: Ai Applications To Pathology In Cardiac Amyloidosismentioning
confidence: 99%
“…Wang et al investigated the ability of the ML approach to detect and classify amyloid depositions in histological slides of the ligamentum flavum preceding the progression to systemic amyloidosis [82]. This model was comparable to the gold standard of manual segmentation either in the training (R = 0.98; p = 0.0033) or the application set of histological images (R = 0.94; p = 0.016) [82]. Kessel et al [83] used corneal tissue sections stained with Congo red, harvested from 42 patients to train and validate a DL algorithm to detect and quantitate the mean amyloid deposits per stromal area.…”
Section: Ai Applications To Pathology In Cardiac Amyloidosismentioning
Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.
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