Dr. Ahmed El Kaffas Profile

Dr. Ahmed El Kaffas

at Oncoustics’ AI Solutions

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SPIE Journal Paper | 5 June 2023

Automated fatty liver disease detection in point-of-care ultrasound B-mode images

Miriam Naim Ibrahim, Raul Blázquez-García, Adi Lightstone, Fankun Meng, Mamatha Bhat, Ahmed El Kaffas, Eranga Ukwatta

JMI, Vol. 10, Issue 03, 034505, (June 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.3.034505

KEYWORDS: Liver, Education and training, Image segmentation, Diseases and disorders, Ultrasonography, Point-of-care devices, Cross validation, Diagnostics, Tissues, Data modeling

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PurposeNon-alcoholic fatty liver disease (NAFLD) is an increasing global health concern, with a prevalence of 25% worldwide. The rising incidence of NAFLD, an asymptomatic condition, reinforces the need for systematic screening strategies in primary care. We present the use of non-expert acquired point-of-care ultrasound (POCUS) B-mode images for the development of an automated steatosis classification algorithm.ApproachWe obtained a Health Insurance Portability and Accountability Act compliant dataset consisting of 478 patients [body mass index 23.60 ± 3.55, age 40.97 ± 10.61], imaged with POCUS by non-expert health care personnel. A U-Net deep learning (DL) model was used for liver segmentation in the POCUS B-mode images, followed by 224 × 224 patch extraction of liver parenchyma. Several DL models including VGG-16, ResNet-50, Inception V3, and DenseNet-121 were trained for binary classification of steatosis. All layers of each tested model were unfrozen, and the final layer was replaced with a custom classifier. Majority voting was applied for patient-level results.ResultsOn a hold-out test set of 81 patients, the final DenseNet-121 model yielded an area under the receiver operator characteristic curve of 90.1%, sensitivity of 95.0%, and specificity of 85.2% for the detection of liver steatosis. Average cross-validation performance in models using patches of liver parenchyma as input outperformed methods using complete B-mode frames.ConclusionsDespite minimal POCUS acquisition training, and low-quality B-mode images, it is possible to detect steatosis using DL algorithms. Implementation of this algorithm in POCUS software may offer an accessible, low-cost steatosis screening technology, for use by non-expert health care personnel.

Proceedings Article | 10 April 2023 Presentation + Paper

Quantitative methods for molecular ultrasound imaging

Negar Sadeghipour, Farbod Tabesh, Arutselvan Natarajan, Meredith Jones, Xuxin Chen, Ramasamy Paulmurugan, Bin Zheng, Ahmed El Kaffas

Proceedings Volume 12470, 124700F (2023) https://doi.org/10.1117/12.2654230

KEYWORDS: Ultrasonography, Tumors, Cancer, Biological imaging, Proteins, Bubbles, Blood vessels, Image enhancement

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Molecular ultrasound imaging is used to image the expression of specific proteins on the surface of blood vessels using the conjugated microbubbles (MBs) that can bind to the targeted proteins, which makes MBs ideal for imaging the protein expressed on blood vessels. However, how to optimally apply MBs in an ultrasound imaging system to detect and quantify the targeted protein expression needs further investigation. To address this issue, objective of this study is to investigate feasibility of developing and applying a new quantitative imaging marker to quantify the expression of protein markers on the surface of cancer cells. To obtain a numeric value proportional to the amount of MBs that bind to the target protein, a standard method for quantification of MBs is applying a destructive pulse, which bursts most of the bubbles in the region of interest. The difference between the signal intensity before and after destruction is used to measure the differential targeted enhancement (dTE). In addition, a dynamic kinetic model is applied to fit the timeintensity curves and a structural similarity model with three metrics is used to detect the differences between images. Study results show that the elevated dTE signals in images acquired from the targeted (MBTar) and isotype (MBIso) are significantly different (p<0.05). Quantitative image features are also successfully computed from the kinetic model and structural similarity model, which provide potential to identify new quantitative image markers that can more accurately differentiate the targeted microbubble status.

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