Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum

Cardiac Amyloidosis: A Comprehensive Review of Pathophysiology, Diagnostic Approach, Applications of Artificial Intelligence, and Management Strategies

Cardiac amyloidosis (CA) is a serious and often fatal condition caused by the accumulation of amyloid fibrils in the heart, leading to progressive heart failure. It involves the misfolding of normally soluble proteins into insoluble amyloid fibrils, with transthyretin and light-chain amyloidosis being the most common forms affecting the heart. Advances in diagnostics, especially cardiac magnetic resonance imaging and non-invasive techniques, have improved early detection and disease management. Artificial intelligence has emerged as a diagnostic tool for cardiac amyloidosis, improving accuracy and enabling earlier intervention through advanced imaging analysis and pattern recognition. Management strategies include volume control, specific pharmacotherapies like tafamidis, and addressing arrhythmias and advanced heart failure. However, further research is needed for novel therapeutic approaches, the long-term effectiveness of emerging treatments, and the optimization of artificial intelligence applications in clinical practice for better patient outcomes. The article aims to provide an overview of CA, outlining its pathophysiology, diagnostic advancements, the role of artificial intelligence, management strategies, and the need for further research.

How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges

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.

Innovative high fat diet establishes a novel zebrafish model for the study of visceral obesity

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.

Lumbar stenosis due to wild-type transthyretin amyloid-induced thickening of the ligamentum flavum: a separate etiology from degeneration of intervertebral discs?

OBJECTIVE

Wild-type transthyretin amyloid (ATTRwt) is deposited in the ligamentum flavum (LF) of a subset of patients with spinal stenosis who undergo decompressive surgery, although its role in the pathophysiology of spinal stenosis is unknown. It has been theorized that degeneration of intervertebral discs causes increased mechanical stress and inflammatory/degenerative cascades and ultimately leads to LF fibrosis. If ATTRwt deposits contribute to LF thickening and spinal stenosis through a different pathway, then patients with ATTRwt may have less severe disc degeneration than those without it. In this study, the authors compared the severity of disc degeneration between patients with lumbar stenosis with and without amyloid in their LF to test whether ATTRwt is a unique contributor to LF thickening and spinal stenosis.

METHODS

Of 324 consecutive patients between 2018 and 2019 who underwent decompression surgery for spinal stenosis and had LF samples sent for pathological analysis, 31 harboring ATTRwt were compared with 88 controls. Patient medical records were retrospectively reviewed for demographic and surgical information. Disc degeneration was assessed on preoperative T2-weighted MR images with the modified Pfirrmann grading system at every lumbar disc level.

RESULTS

Baseline characteristics were similar between the groups, except for a statistically significant increase in age in the ATTRwt group. The crude unadjusted comparisons between the groups trended toward a less severe disc degeneration in the ATTRwt group, although this difference was not statistically significant. A multivariable linear mixed-effects model was created to adjust for the effects of age and to isolate the influence of ATTRwt, the presence of an operation at the level, and the specific disc level (between L1 and S1). This model revealed that ATTRwt, the presence of an operation, and the specific level each had significant effects on modified Pfirrmann scores.

CONCLUSIONS

Less severe disc degeneration was noted in patients with degenerative spinal stenosis harboring ATTRwt compared with those without amyloid. This finding suggests that ATTRwt deposition may play a separate role in LF thickening from that played by disc degeneration. Future studies should aim to elucidate this potentially novel pathophysiological pathway, which may uncover an exciting potential for the development of amyloid-targeted therapies that may help slow the development of spinal stenosis.

The Relationship Between Wild-Type Transthyretin Amyloid Load and Ligamentum Flavum Thickness in Lumbar Stenosis Patients

BACKGROUND

One key contributor to lumbar stenosis is thickening of the ligamentum flavum (LF), a process still poorly understood. Wild-type transthyretin amyloid (ATTRwt) has been found in the LF of patients undergoing decompression surgery, suggesting that amyloid may play a role. However, it is unclear whether within patients harboring ATTRwt, the amount of amyloid is associated with LF thickness.

METHODS

From an initial cohort of 324 consecutive lumbar stenosis patients whose LF specimens from decompression surgery were sent for analysis (2018-2019), 33 patients met the following criteria: 1) Congo red-positive amyloid in the LF, 2) ATTRwt by mass spectrometry-based proteomics, and 3) an available preoperative magnetic resonance imaging. Histological specimens were digitized, and amyloid load was quantified through Trainable Weka Segmentation machine learning. LF thicknesses were manually measured on axial T2-weighted preoperative magnetic resonance imaging scans at each lumbar level, L1-S1. The sum of thicknesses at every lumbar LF level (L1-S1) equals "lumbar LF burden".

RESULTS

Patients had a mean age of 72.7 years (range = 59-87), were mostly male (61%) and white (82%), and predominantly had surgery at L4-L5 levels (73%). Amyloid load was positively correlated with LF thickness (R = 0.345, P = 0.0492) at the levels of surgical decompression. Furthermore, amyloid load was positively correlated with lumbar LF burden (R = 0.383, P = 0.0279).

CONCLUSIONS

Amyloid load is positively correlated with LF thickness and lumbar LF burden across all lumbar levels, in a dose-dependent manner. Further studies are needed to validate these findings, uncover the underlying pathophysiology, and pave the way toward using therapies that slow LF thickening.