Dr. Arjun Kalyanpur

PACS/VNA
4 benefits of teleradiology in the coronavirus pandemic
Over the years, the benefits of teleradiology have become well-established. But the novel coronavirus pandemic has made teleradiology even more essential, according to Dr. Arjun Kalyanpur of Teleradiology Solutions.
April 19, 2020
2020 04 16 18 17 3383 Radiologist Ct Scan Brain Tumor 400
Practice Management
6 life lessons for radiologists from Roger Federer
What does Roger Federer have to do with radiology? Lots, according to Dr. Arjun Kalyanpur of Teleradiology Solutions. His analysis of the tennis great's training regimen and lifestyle reveals six life lessons that radiologists should try to emulate if they want to remain productive and continue to enjoy their profession.
February 21, 2018
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Pediatric Imaging
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Patient-friendly radiology reports improve family comprehension
Terminology in radiology reports can be difficult for patients and families to comprehend.
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Early CTA leads to improved stroke outcomes for patients with BCVI
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Imaging turnaround times continued to climb in 2024
(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.
Breast
DBT-based foundational model classifies breast density
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Patient-friendly radiology reports improve family comprehension
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DBT-based foundational model classifies breast density
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DBT-DINO showed strong performance in classifying breast density from DBT images.
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(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.
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ED patients prefer empathetic communication for incidental findings
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Incorporating patient preferences into communication may improve understanding and follow-up in addressing incidental findings.
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