Moussallem Lauren, Lombardi Lisa, McGuinness Myra B, Kolic Maria, Baglin Elizabeth K, Jin Rui, Habili Nariman, Kvansakul Jessica, Titchener Samuel A, Abbott Carla J, Walker Janine G, Allen Penelope J, Petoe Matthew A, Barnes Nick
Centre for Eye Research Australia, Royal Victorian Eye & Ear Hospital, Melbourne, VIC, Australia.
Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, VIC, Australia.
J Neural Eng. 2025 Apr 24;22(2). doi: 10.1088/1741-2552/adc83a.
To evaluate the effectiveness of a novel depth-based vision processing (VP) method, local background enclosure (LBE), in comparison to the comprehensive VP method, Lanczos2 (L2), in suprachoroidal retinal prosthesis implant recipients during navigational tasks in laboratory and real-world settings.Four participants were acclimatized to both VP methods. Participants were asked to detect and navigate past five of eight possible obstacles in a white corridor across 20-30 trials. Randomized obstacles included black or white mannequins, black or white overhanging boxes, black or white bins and black or white stationary boxes. The same four participants underwent trials at three different real-word urban locations using both VP methods (randomized order). They were tasked with navigating a complex, dynamic pre-determined scene while detecting, verbally identifying, and avoiding obstacles in their path.The indoor obstacle course showed that the LBE method (63.6 ± 10.7%, mean ± SD) performed significantly better than L2 (48.5 ± 11.2%), for detection of obstacles (< 0.001, Mack-Skillings). The real-world assessment showed that of the objects detected, 50.2% (138/275) were correctly identified using LBE and 41.7% (138/331) using L2, corresponding to a risk difference of 8 percentage points,= 0.081).Real world outcomes can be improved using an enhanced VP algorithm, providing depth-based visual cues (#NCT05158049).
为评估一种新型基于深度的视觉处理（VP）方法——局部背景包围（LBE），与综合VP方法兰索斯2（L2）相比，在脉络膜上视网膜假体植入受者于实验室和现实环境中的导航任务期间的有效性。四名参与者适应了这两种VP方法。要求参与者在一条白色走廊中检测并绕过八个可能障碍物中的五个，进行20 - 30次试验。随机障碍物包括黑色或白色人体模型、黑色或白色悬垂箱、黑色或白色箱子以及黑色或白色固定箱。相同的四名参与者在三个不同的现实城市地点使用这两种VP方法（随机顺序）进行试验。他们的任务是在检测、口头识别并避开路径中的障碍物的同时，在一个复杂、动态的预先确定场景中导航。室内障碍课程显示，在检测障碍物方面，LBE方法（63.6 ± 10.7%，平均值±标准差）的表现明显优于L2（48.5 ± 11.2%）（< 0.001，Mack - Skillings）。现实世界评估显示，在检测到的物体中，使用LBE正确识别的比例为50.2%（138/275），使用L2为41.7%（138/331），风险差异为8个百分点（= 0.081）。使用增强的VP算法可以改善现实世界的结果，提供基于深度的视觉线索（#NCT05158049）。
