Echocardiography (Mount Kisco, N.Y.) · 2026
A Clinician-Led Feasibility Study for Echocardiographic View Classification Using Standard Workstation Hardware and Routine Clinical Archives.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Xie Jinjie, Wang Yaxin, Liu Wei
Аннотация
PURPOSE: To evaluate whether a frontline echocardiographer, without engineering support or dedicated research hardware, can independently develop a validated five-class echocardiographic view classifier using artificial intelligence-assisted coding, standard departmental workstation hardware, and routine clinical archives.
METHODS: A clinician used artificial intelligence-assisted coding (Kimi 2.6, Moonshot AI) to develop a pipeline on a standard Tomtec echocardiographic analysis workstation (Intel Core i7, NVIDIA RTX 4060 8 GB VRAM, 32 GB RAM) running Ubuntu 22.04 LTS via Windows Subsystem for Linux 2. From 1387 transthoracic echocardiograms, 9796 B-mode end-diastolic frames were extracted. A batch annotation strategy was employed: 466 seed images were annotated to train an overfitted EfficientNet-B0 screener, which pre-classified 5029 high-confidence standard-view frames. These were batch-reviewed in approximately 24 min, yielding 5028 verified frames from 1242 patients, partitioned into training (n = 3545), validation (n = 731), and internal test (n = 752) sets. A five-class ResNet-18 classifier was trained and evaluated on the primary dataset and evaluated on an external validation dataset (74 patients, 263 frames) without retraining.
RESULTS: The total expert annotation burden was 32 min. The classifier achieved 98.27% accuracy (95% CI: 97.06%-99.08%) on the internal test set with a macro F1 of 0.9784. On external validation, overall accuracy was 71.48% (95% CI: 65.61%-76.86%) with a macro F1 of 0.7100. Parasternal short-axis and apical two-chamber views generalized robustly (100% and 94.23%, respectively), whereas apical four-chamber, apical three-chamber, and parasternal long-axis views showed lower accuracy due to device-specific image processing differences.
CONCLUSION: Clinical artificial intelligence development in echocardiography is substantially more accessible than conventionally assumed. A single clinician using existing hardware and artificial intelligence-assisted coding can produce a validated view classifier with minimal annotation burden, providing a practical roadmap for in-house customization.
METHODS: A clinician used artificial intelligence-assisted coding (Kimi 2.6, Moonshot AI) to develop a pipeline on a standard Tomtec echocardiographic analysis workstation (Intel Core i7, NVIDIA RTX 4060 8 GB VRAM, 32 GB RAM) running Ubuntu 22.04 LTS via Windows Subsystem for Linux 2. From 1387 transthoracic echocardiograms, 9796 B-mode end-diastolic frames were extracted. A batch annotation strategy was employed: 466 seed images were annotated to train an overfitted EfficientNet-B0 screener, which pre-classified 5029 high-confidence standard-view frames. These were batch-reviewed in approximately 24 min, yielding 5028 verified frames from 1242 patients, partitioned into training (n = 3545), validation (n = 731), and internal test (n = 752) sets. A five-class ResNet-18 classifier was trained and evaluated on the primary dataset and evaluated on an external validation dataset (74 patients, 263 frames) without retraining.
RESULTS: The total expert annotation burden was 32 min. The classifier achieved 98.27% accuracy (95% CI: 97.06%-99.08%) on the internal test set with a macro F1 of 0.9784. On external validation, overall accuracy was 71.48% (95% CI: 65.61%-76.86%) with a macro F1 of 0.7100. Parasternal short-axis and apical two-chamber views generalized robustly (100% and 94.23%, respectively), whereas apical four-chamber, apical three-chamber, and parasternal long-axis views showed lower accuracy due to device-specific image processing differences.
CONCLUSION: Clinical artificial intelligence development in echocardiography is substantially more accessible than conventionally assumed. A single clinician using existing hardware and artificial intelligence-assisted coding can produce a validated view classifier with minimal annotation burden, providing a practical roadmap for in-house customization.