Alaa Eldin Abdelaal

All Publications

• Parallelism in Autonomous Robotic Surgery IEEE ROBOTICS AND AUTOMATION LETTERS Abdelaal, A., Liu, J., Hong, N., Hager, G. D., Salcudean, S. E. 2021; 6 (2): 1824-1831

  View details for DOI 10.1109/LRA.2021.3060402

  View details for Web of Science ID 000629028400017

• Robotics In Vivo: A Perspective on Human-Robot Interaction in Surgical Robotics ANNUAL REVIEW OF CONTROL, ROBOTICS, AND AUTONOMOUS SYSTEMS, VOL 3, 2020 Abdelaal, A., Mathur, P., Salcudean, S. E., Leonard, N. E. 2020; 3: 221-242

  View details for DOI 10.1146/annurev-control-091219-013437

  View details for Web of Science ID 000534341200009

• A "pickup" stereoscopic camera with visual-motor aligned control for the da Vinci surgical system: a preliminary study Avinash, A., Abdelaal, A., Mathur, P., Salcudean, S. E. SPRINGER HEIDELBERG. 2019: 1197-1206

  Abstract

  The current state-of-the-art surgical robotic systems use only a single endoscope to view the surgical field. Research has been conducted to introduce additional cameras to the surgical system, giving rise to new camera angles that cannot be achieved using the endoscope alone. While this additional visualization certainly aids in surgical performance, current systems lack visual-motor compatibility with respect to the additional camera views. We propose a new system that overcomes this limitation.In this paper, we introduce a novel design of an additional "pickup" camera that can be integrated into the da Vinci Surgical System. We also introduce a solution to work comfortably in the various arbitrary views this camera provides by eliminating visual-motor misalignment. This is done by changing the working frame of the surgical instruments to work with respect to the coordinate system at the "pickup" camera instead of the endoscope.Human user trials ([Formula: see text]) were conducted to evaluate the effect of visual-motor alignment with respect to the "pickup" camera on surgical performance. An inanimate surgical peg transfer task from the validated Fundamentals of Laparoscopic Surgery (FLS) Training Curriculum was used, and an improvement of 73% in task completion time and 80% in accuracy was observed with the visual-motor alignment over the case without it.Our study shows that there is a requirement to achieve visual-motor alignment when utilizing views from external cameras in current clinical surgical robotics setups. We introduce a complete system that provides additional camera views with visual-motor aligned control. Such a system would be useful in existing surgical procedures and could also impact surgical planning and navigation.

  View details for DOI 10.1007/s11548-019-01955-9

  View details for Web of Science ID 000471635000010

  View details for PubMedID 31056727

• Play Me Back: A Unified Training Platform for Robotic and Laparoscopic Surgery IEEE ROBOTICS AND AUTOMATION LETTERS Abdelaal, A., Sakr, M., Avinash, A., Mohammed, S. K., Bajwa, A., Sahni, M., Hor, S., Fels, S., Salcudean, S. E. 2019; 4 (2): 554–61

  View details for DOI 10.1109/LRA.2018.2890209

  View details for Web of Science ID 000457917800001

• Head motion-corrected eye gaze tracking with the da Vinci surgical system INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Banks, A., Abdelaal, A., Salcudean, S. 2024

  Abstract

  To facilitate the integration of point of gaze (POG) as an input modality for robot-assisted surgery, we introduce a robust head movement compensation gaze tracking system for the da Vinci Surgical System. Previous surgical eye gaze trackers require multiple recalibrations and suffer from accuracy loss when users move from the calibrated position. We investigate whether eye corner detection can reduce gaze estimation error in a robotic surgery context.A polynomial regressor is first used to estimate POG after an 8-point calibration, and then, using another regressor, the POG error from head movement is estimated from the shift in 2D eye corner location. Eye corners are computed by first detecting regions of interest using the You Only Look Once (YOLO) object detector trained on 1600 annotated eye images (open dataset included). Contours are then extracted from the bounding boxes and a derivative-based curvature detector refines the eye corner.Through a user study (n = 24), our corner-contingent head compensation algorithm showed an error reduction in degrees of visual angle of 1.20 ∘ (p = 0.037) for the left eye and 1.26 ∘ (p = 0.079) for the right compared to the previous gold-standard POG error correction method. In addition, the eye corner pipeline showed a root-mean-squared error of 3.57 (SD = 1.92) pixels in detecting eye corners over 201 annotated frames.We introduce an effective method of using eye corners to correct for eye gaze estimation, enabling the practical acquisition of POG in robotic surgery.

  View details for DOI 10.1007/s11548-024-03173-4

  View details for Web of Science ID 001250319900003

  View details for PubMedID 38888820

• Haptics: The Science of Touch as a Foundational Pathway to Precision Education and Assessment. Academic medicine : journal of the Association of American Medical Colleges Perrone, K., Abdelaal, A. E., Pugh, C., Okamura, A. 2023

  Abstract

  Clinical touch is the cornerstone of the doctor-patient relationship and can impact patient experience and outcomes. In the current era, driven by an ever-increasing infusion of point of care technologies, physical exam skills have become undervalued. Moreover, touch and hands-on skills have been difficult to teach due to inaccurate assessments and difficulty with learning transfer through observation. In this article, the authors argue that haptics, the science of touch, provides a unique opportunity to explore new pathways to facilitate touch training. Furthermore, haptics can dramatically increase the density of touch-based assessments without increasing human rater burden-essential for realizing precision assessment. The science of haptics is reviewed, including the benefits of using haptics-informed language for objective structured clinical examinations. The authors describe how haptic devices and haptic language have and can be used to facilitate learning, communication, documentation and a much-needed reinvigoration of physical examination and touch excellence at the point of care. The synergy of haptic devices, artificial intelligence, and virtual reality environments are discussed. The authors conclude with challenges of scaling haptic technology in medical education, such as cost and translational needs, and opportunities to achieve wider adoption of this transformative approach to precision education.

  View details for DOI 10.1097/ACM.0000000000005607

  View details for PubMedID 38109654

• Orientation Matters: 6-DoF Autonomous Camera Movement for Video-based Skill Assessment in Robot-Assisted Surgery Abdelaal, A., Hong, N., Avinash, A., Budihal, D., Sakr, M., Hager, G. D., Salcudean, S. E., IEEE IEEE. 2022

  View details for DOI 10.1109/BIOROB52689.2022.9925374

  View details for Web of Science ID 000920393600050

• A multi-camera, multi-view system for training and skill assessment for robot-assisted surgery INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Abdelaal, A., Avinash, A., Kalia, M., Hager, G. D., Salcudean, S. E. 2020; 15 (8): 1369-1377

  Abstract

  This paper introduces the concept of using an additional intracorporeal camera for the specific goal of training and skill assessment and explores the benefits of such an approach. This additional camera can provide an additional view of the surgical scene, and we hypothesize that this additional view would improve surgical training and skill assessment in robot-assisted surgery.We developed a multi-camera, multi-view system, and we conducted two user studies ([Formula: see text]) to evaluate its effectiveness for training and skill assessment. In the training user study, subjects were divided into two groups: a single-view group and a dual-view group. The skill assessment study was a within-subject study, in which every subject was shown single- and dual view recorded videos of a surgical training task, and the goal was to count the number of errors committed in each video.The results show the effectiveness of using an additional intracorporeal camera view for training and skill assessment. The benefits of this view are modest for skill assessment as it improves the assessment accuracy by approximately 9%. For training, the additional camera view is clearly more effective. Indeed, the dual-view group is 57% more accurate than the single-view group in a retention test. In addition, the dual-view group is 35% more accurate and 25% faster than the single-view group in a transfer test.A multi-camera, multi-view system has the potential to significantly improve training and moderately improve skill assessment in robot-assisted surgery. One application of our work is to include an additional camera view in existing virtual reality surgical training simulators to realize its benefits in training. The views from the additional intracorporeal camera can also be used to improve on existing surgical skill assessment criteria used in training systems for robot-assisted surgery.

  View details for DOI 10.1007/s11548-020-02176-1

  View details for Web of Science ID 000534201600001

  View details for PubMedID 32430693

• Evaluation of Increasing Camera Baseline on Depth Perception in Surgical Robotics Avinash, A., Abdelaal, A., Salcudean, S. E., IEEE IEEE. 2020: 5509-5515

  View details for Web of Science ID 000712319503119

• Multimodal Training by Demonstration for Robot-Assisted Surgery Abdelaal, A., Hager, G. D., Salcudean, S. E., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2020: 549-551

  View details for DOI 10.1145/3371382.3377448

  View details for Web of Science ID 000643728500181

• Event-based Control as a Cloud Service Abdelaal, A., Hegazy, T., Hefeeda, M., IEEE IEEE. 2017: 1017-1023

  View details for Web of Science ID 000427033301011

• LOST Highway: a Multiple-Lane Ant-Trail Algorithm to Reduce Congestion in Large-Population Multi-Robot Systems Abdelaal, A., Sakr, M., Vaughan, R., IEEE IEEE. 2017: 161-167

  View details for DOI 10.1109/CRV.2017.24

  View details for Web of Science ID 000425894100022