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最少侵入性上腺切除术中的人工智能:使用深度学习来识别左侧上腺静脉

Berke Sengun¹, Yalin Iscan¹, Gozde A Tataroglu Ozbulak²

¹Department of General Surgery, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey.

Surgical laparoscopy, endoscopy & percutaneous techniques

|June 13, 2023

概括

人工智能在微创性上腺切除术期间准确识别左上腺静脉. 深度学习模型提供了实时手术指导的潜力,提高了患者的安全性.

科学领域:

手术创新在外科创新.
医疗成像医学成像
人工智能在医学中的应用

背景情况:

至少侵入性上腺切除术是对上腺质量的标准.
精确的鉴定和上腺静脉的绑定是关键的手术步骤.
人工智能和深度学习可以在手术期间提供实时解剖指导.

研究的目的:

开发和评估人工智能模型用于左上腺静脉的语义细分.
评估使用深度学习在上腺手术中识别关键解剖学的可行性.

主要方法:

从最小侵入性横腹左上腺切除术的手术内视频的回顾性分析.
使用高效的阶段性特征金字塔网络 (ESFPNet) 开发深度学习模型.
使用子相似系数 (DSC) 和交叉与联合的分段精度的评估.

主要成果:

人工智能模型实现了0.77的平均DSC和0.82.8的灵敏度.
性能最高的模型达到了0.93.9的最大DSC.
结果表明成功预测左上腺静脉解剖学.

结论:

深度学习算法在预测左上腺静脉解剖学方面表现出很高的性能.
人工智能有可能在上腺手术期间提供实时指导.
未来的应用可能会提高外科准确性和安全性.

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