Stony Brook Emergency Ultrasound Fellowship

Congratulations to Our
SonoGames Team!

“It’s TEE Time”— Top 20 Finish

Our Stony Brook team competed against over 300 emergency medicine residents from programs across the country at the SAEM 2026 Annual Meeting in Atlanta — and made it to the Top 20!

Team Ready

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Scanning Challenge

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Celebrating!

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What is SonoGames?

SonoGames® is the premier national point-of-care ultrasound competition held annually at the Society for Academic Emergency Medicine (SAEM) conference. Over 300 EM residents from across the country compete in a 4-hour gauntlet of scanning challenges that test precision, clinical knowledge, decision-making, and teamwork. Teams wear themed costumes and battle through three pulse-pounding rounds for the coveted SonoCup and the title of SonoChamps.

Ultrasound Case of the Week

Refreshes every week

Cardiac POCUS

Cardiac Tamponade

Clinical Scenario

A 62-year-old male with metastatic lung cancer presents with progressive dyspnea, muffled heart sounds, and JVD. BP is 88/62 with pulsus paradoxus of 18 mmHg.

Ultrasound Findings

Subxiphoid cardiac view shows a large circumferential pericardial effusion with diastolic collapse of the right atrium and right ventricle. The IVC is plethoric (>2.1 cm) without respiratory variation. Swinging heart motion is noted.

Trivia Question of the Week

New question every week · Sono Playground →

Create a username to track your score on the leaderboard:

Lung Ultrasound

B-lines in lung ultrasound are best described as:

Leaderboard

No scores yet. Be the first to answer!

Sono Playground

Literature Spotlight

Recent POCUS publication in Emergency Medicine

AI-Enhanced Point-of-Care Ultrasound in Emergency Care

Puticiu et al. · Journal of Clinical Medicine · January 2026

This review explores how artificial intelligence is transforming point-of-care ultrasound in the emergency department. The authors examine AI-assisted image interpretation for cardiac, lung, and abdominal POCUS applications, including automated ejection fraction estimation, pneumothorax detection, and free fluid identification. Key findings suggest that AI-augmented POCUS can reduce interpretation errors by novice sonographers by up to 30% and decrease time-to-diagnosis in critical presentations. The paper highlights both the promise and current limitations of AI integration, emphasizing that these tools should augment — not replace — clinical decision-making.

Read Full Article

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