Peter Kamel

A Systematic Evaluation of Image Preprocessing in Deep Learning Detection and Segmentation of Intracranial Metastatic Disease

Peter Kamel, Ahmed Naeem, Komal Shah, Max Wintermark

BACKGROUND AND PURPOSE: Image preprocessing is an essential, though often overlooked, part of machine learning, and it is unclear how preprocessing techniques affect metastatic disease segmentation. This is particularly true given the differences between segmentation of primary brain tumors and the detection …

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Chronic Subdural Hematoma Segmentation: A Dedicated Model to Overcome the Limitations of Acute Hemorrhage Segmentation Across Chronic Subdural Hematoma Subtypes and Density Variations

Bhavya Reddy, Ritvik Mutyam, Thorsten R. Fleiter, David Dreizin et autres

Most existing intracranial hematoma segmentation models target acute hemorrhages and may not generalize to the heterogeneous morphology of chronic subdural hematomas (CSDH). We compared a model trained on an open-access acute intracranial hemorrhage dataset with a model trained in combination with a …

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Artificial Intelligence in Stroke Imaging: A Review of Current Applications and Limitations

Peter Kamel, Max Wintermark

Stroke is a major global health burden, requiring time-sensitive diagnosis and treatment to improve patient outcomes. This urgency has created a compelling role for artificial intelligence in the stroke imaging workflow to accelerate diagnosis and treatment. Artificial intelligence has demonstrated a significant …

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Standardizing Heterogeneous MRI Series Description Metadata Using Large Language Models

Peter Kamel, Florence X. Doo, Dharmam Savani, Adway Kanhere et autres

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2100 Predicting Hospitalization and Clinical Outcomes in Diffuse Axonal Injury Using Machine Learning Lesion Segmentation

Annie Trang, Konrad Walek, Jamie Podell, Uttam K. Bodanapally et autres

INTRODUCTION: Diffuse Axonal Injury (DAI) is graded on MRI using the Adams Classification, which has limited prognostic utility. Machine Learning (ML) is a promising tool to detect more granular lesions to predict clinical outcomes. METHODS: The study included patients diagnosed with DAI …

A Comparative Evaluation of Large Language Model Utility in Neuroimaging Clinical Decision Support

Luke C. Miller, Peter Kamel, Jigar B. Patel, Min Zhan et autres

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NIMG-47. MULTI-INSTITUTIONAL VALIDATION OF AN AI-BASED MODEL FOR PREDICTION OF TUMOR INFILTRATION AND FUTURE RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: RESULTS FROM THE RESPOND CONSORTIUM

Suyash Mohan, José García, Hamed Akbari, Sunwoo Kwak et autres

Abstract BACKGROUND Glioblastoma is an infiltrative primary brain tumor with poor prognosis despite multimodal therapy. Recurrence is inevitable secondary to tumor cell infiltration in the peritumoral tissues, beyond contrast enhancing margins, which is the target for surgical resection. We hypothesize that a …

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Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts

Peter Kamel, Mazhar Khalid, Rachel Steger, Adway Kanhere et autres

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Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences

Peter Kamel, Adway Kanhere, Pranav Kulkarni, Mazhar Khalid et autres

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Evaluation of Multimodal ChatGPT (GPT-4V) in Describing Mammography Image Features

Hana L. Haver, Manisha Bahl, Florence X. Doo, Peter Kamel et autres

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Radiologic evaluation of skull base masses

Licia Luna, Peter Kamel, Rohini N. Nadgir

Contributors

Eric H. Abello, Arash Abiri, Nithin D. Adappa, Meleha Ahmad et autres

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