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Profil bibliographique

Maryam Al‐Hasani

Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.

7Publications signalées
51Citations signalées
1Affiliations récentes

Les institutions déclarées

Les domaines associés

Hepatocellular Carcinoma Treatment and PrognosisLiver Disease Diagnosis and TreatmentUltrasound and Hyperthermia ApplicationsUltrasound in Clinical ApplicationsPhotoacoustic and Ultrasonic Imaging

Les publications récentes

Accès ouvert 2026 article OpenAlex

Ultrasound Radiomics in Pediatric Imaging: Current Applications, Challenges, and Future Directions Toward Clinical Implementation

Maria Mezher, Mohannad Elgamal, Sean Schoeman, Maryam Al‐Hasani et autres

Ultrasound is widely used in pediatric imaging because it is safe, portable, real-time, and free of ionizing radiation, but interpretation remains qualitative and operator-dependent. Ultrasound radiomics can extract quantitative features from standard grayscale images, providing potential biomarkers of tissue patterns not readily …

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0 citations Diagnostics
Accès ouvert 2024 article OpenAlex

Advanced Techniques for Liver Fibrosis Detection: Spectral Photoacoustic Imaging and Superpixel Photoacoustic Unmixing Analysis for Collagen Tracking

Laith R. Sultan, Valeria Grasso, Jithin Jose, Maryam Al‐Hasani et autres

Liver fibrosis, a major global health issue, is marked by excessive collagen deposition that impairs liver function. Noninvasive methods for the direct visualization of collagen content are crucial for the early detection and monitoring of fibrosis progression. This study investigates the potential …

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10 citations Sensors
Accès ouvert 2023 article OpenAlex

Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound

Laith R. Sultan, Allison Haertter, Maryam Al‐Hasani, George Demiris et autres

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this …

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12 citations AI
Accès ouvert 2023 article OpenAlex

Hydralazine‐augmented contrast ultrasound imaging improves the detection of hepatocellular carcinoma

Laith R. Sultan, Mrigendra B. Karmacharya, Maryam Al‐Hasani, Theodore W. Cary et autres

BACKGROUND: Hepatocellular carcinoma (HCC) detection with B-mode and contrast-enhanced ultrasound (CUS) imaging often varies between subjects, especially in patients with background cirrhosis. Various factors contribute to this variability, including the tumor blood flow, tumor size, internal echoes, and its location in livers …

us (code pays fourni par la source)

1 citation Medical Physics
Accès ouvert 2022 article OpenAlex

Ultrasound Radiomics for the Detection of Early-Stage Liver Fibrosis

Maryam Al‐Hasani, Laith R. Sultan, Hersh Sagreiya, Theodore W. Cary et autres

Objective: The study evaluates quantitative ultrasound (QUS) texture features with machine learning (ML) to enhance the sensitivity of B-mode ultrasound (US) for the detection of fibrosis at an early stage and distinguish it from advanced fibrosis. Different ML methods were evaluated to …

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21 citations Diagnostics
Accès ouvert 2022 conference-paper OpenAlex

Contrast-enhanced ultrasound for assessing blood flow modulation of hepatocellular carcinoma by hydralazine

Laith R. Sultan, Maryam Al‐Hasani, Mrigendra B. Karmacharya, Theodore W. Cary et autres

Modulating aberrant tumor microvasculature provides unique opportunities for enhancing ultrasound imaging of hepatocellular carcinoma (HCC). This study aims to use contrast-enhanced ultrasound to evaluate the potential of a potent vasodilator, hydralazine, to attenuate blood flow in HCC while enhancing it in the …

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0 citations 2022 IEEE International Ultrasonics Symposium (IUS)
Accès ouvert 2022 article OpenAlex

Can Sequential Images from the Same Object Be Used for Training Machine Learning Models? A Case Study for Detecting Liver Disease by Ultrasound Radiomics

Laith R. Sultan, Theodore W. Cary, Maryam Al‐Hasani, Mrigendra B. Karmacharya et autres

Machine learning for medical imaging not only requires sufficient amounts of data for training and testing but also that the data be independent. It is common to see highly interdependent data whenever there are inherent correlations between observations. This is especially to …

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7 citations AI

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