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Objectives The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction. Materials and Methods The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predicting retinal age. The framework explores federated learning (FL) as well as traveling model (TM) approaches for distributed training of the linear regression head. To evaluate this framework, we simulate a client network using fundus image data from the UK Biobank. Additionally, data from patients with type 1 diabetes from the UK Biobank and the Brazilian Multilabel Ophthalmological Dataset (BRSET) were utilized to explore the clinical utility of the developed methods. Results Our findings reveal that the developed distributed learning framework achieves retinal age prediction performance on par with centralized methods, with FL and TM providing similar performance (mean absolute error of 3.57 ± 0.18 years for centralized learning, 3.60 ± 0.16 years for TM, and 3.63 ± 0.19 years for FL). Notably, the TM was found to converge with fewer local updates than FL. Moreover, patients with type 1 diabetes exhibited significantly higher RAG values than healthy controls in all models, for both the UK Biobank and BRSET datasets (P  < .001). Discussion The high computational and memory efficiency of the developed distributed learning framework makes it well suited for resource-constrained environments. Conclusion The capacity of this framework to integrate data from underrepresented populations for training of retinal age prediction models could significantly enhance the accessibility of the RAG as an important disease biomarker. [ABSTRACT FROM AUTHOR]

Objective Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. Materials and Methods Our framework utilizes synthetic neuroimages with known disease effects and sources of bias. We evaluated the impact of bias effects and the efficacy of 3 bias mitigation strategies in counterfactual data scenarios on a convolutional neural network (CNN) classifier. Results The analysis revealed that training a CNN model on the datasets containing bias effects resulted in expected subgroup performance disparities. Moreover, reweighing was the most successful bias mitigation strategy for this setup. Finally, we demonstrated that explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Discussion The value of this framework is showcased in our findings on the impact of bias scenarios and efficacy of bias mitigation in a deep learning model pipeline. This systematic analysis can be easily expanded to conduct further controlled in silico trials in other investigations of bias in medical imaging AI. Conclusion Our novel methodology for objectively studying bias in medical imaging AI can help support the development of clinical decision-support tools that are robust and responsible. [ABSTRACT FROM AUTHOR]

Obesity-related conditions including heart disease, stroke, and type 2 diabetes are leading causes of preventable death. Recent evidence suggests that altered myocellular lipid metabolism in obesity may lead to increased insulin resistance (IR) that predisposes to these disorders. To test the hypothesis that muscles rich in type I vs. type II muscle fibers would exhibit similar changes in intramyocellular lipid (IMCL) and extramyocellular lipid (EMCL) content in obesity, we utilized a new four-dimensional multi echo echo-planar correlated spectroscopic imaging technique that allows separate determination of IMCL and EMCL content in individual calf muscles in obese vs. normal healthy human subjects. Calf muscles were scanned in 32 obese and 11 healthy subjects using a 3T MRI/MRS scanner, and IR in the obese subjects was documented by glucose tolerance testing. In obese subjects, elevation of both IMCL and EMCL content was observed in the gastrocnemius and tibialis anterior muscles (with mixed type I and II fiber content), while a significant increase in only IMCL content (+48%, p

PurposeTo measure cerebral metabolite levels in perinatally HIV-infected youths and healthy controls using the accelerated five dimensional (5D) echo planar J-resolved spectroscopic imaging (EP-JRESI) sequence, which is capable of obtaining two dimensional (2D) J-resolved spectra from three spatial dimensions (3D).Materials and methodsAfter acquisition and reconstruction of the 5D EP-JRESI data, T1-weighted MRIs were used to classify brain regions of interest for HIV patients and healthy controls: right frontal white (FW), medial frontal gray (FG), right basal ganglia (BG), right occipital white (OW), and medial occipital gray (OG). From these locations, respective J-resolved and TE-averaged spectra were extracted and fit using two different quantitation methods. The J-resolved spectra were fit using prior knowledge fitting (ProFit) while the TE-averaged spectra were fit using the advanced method for accurate robust and efficient spectral fitting (AMARES).ResultsQuantitation of the 5D EP-JRESI data using the ProFit algorithm yielded significant metabolic differences in two spatial locations of the perinatally HIV-infected youths compared to controls: elevated NAA/(Cr+Ch) in the FW and elevated Asp/(Cr+Ch) in the BG. Using the TE-averaged data quantified by AMARES, an increase of Glu/(Cr+Ch) was shown in the FW region. A strong negative correlation (r < -0.6) was shown between tCh/(Cr+Ch) quantified using ProFit in the FW and CD4 counts. Also, strong positive correlations (r > 0.6) were shown between Asp/(Cr+Ch) and CD4 counts in the FG and BG.ConclusionThe complimentary results using ProFit fitting of J-resolved spectra and AMARES fitting of TE-averaged spectra, which are a subset of the 5D EP-JRESI acquisition, demonstrate an abnormal energy metabolism in the brains of perinatally HIV-infected youths. This may be a result of the HIV pathology and long-term combinational anti-retroviral therapy (cART). Further studies of larger perinatally HIV-infected cohorts are necessary to confirm these findings.