BACKGROUND: Robotic technology is an important tool in surgical innovation, with robots increasingly being used in the clinical setting. Robots can be used to enhance accuracy, perform remote actions, or to automate tasks. One such surgical task is suturing, a repetitive, fundamental component of surgery that can be tedious and time consuming. Suturing is a promising automation target because of its ubiquity, repetitive nature, and defined constraints. This systematic review examines research to date on autonomous suturing.
METHODS: A systematic review of the literature focused on autonomous suturing was conducted in accordance with PRISMA guidelines.
RESULTS: 6850 articles were identified by searching PubMed, Embase, Compendex, and Inspec. Duplicates and non-English articles were removed. 4389 articles were screened and 4305 were excluded. Of the 84 remaining, 43 articles did not meet criteria, leaving 41 articles for final review. Among these, 34 (81%) were published after 2014. 31 (76%) were published in an engineering journal9 in a robotics journal, and 1 in a medical journal. The great majority of articles (33, 80%) did not have a specific clinical specialty focus, whereas 6 (15%) were focused on applications in MIS/laparoscopic surgery and 2 (5%) on applications in ophthalmology. Several suturing subtasks were identified, including knot tying, suture passing/needle insertion, needle passing, needle and suture grasping, needle tracking/kinesthesia, suture thread detection, suture needle shape production, instrument assignment, and suture accuracy. 14 articles were considered multi-component because they referred to several previously mentioned subtasks.
CONCLUSIONS: In this systematic review exploring research to date on autonomous suturing, 41 articles demonstrated significant progress in robotic suturing. This summary revealed significant heterogeneity of work, with authors focused on different aspects of suturing and a multitude of engineering problems. The review demonstrates increasing academic and commercial interest in surgical automation, with significant technological advances toward feasibility.

Surgical resection is the current clinical standard of care for treating squamous cell carcinoma. Maintaining an adequate tumor resection margin is the key to a good surgical outcome, but tumor edge delineation errors are inevitable with manual surgery due to difficulty in visualization and hand-eye coordination. Surgical automation is a growing field of robotics to relieve surgeon burdens and to achieve a consistent and potentially better surgical outcome. This paper reports a novel robotic supervised autonomous electrosurgery technique for soft tissue resection achieving millimeter accuracy. The tumor resection procedure is decomposed to the subtask level for a more direct understanding and automation. A 4-DOF suction system is developed, and integrated with a 6-DOF electrocautery robot to perform resection experiments. A novel near-infrared fluorescent marker is manually dispensed on cadaver samples to define a pseudotumor, and intraoperatively tracked using a dual-camera system. The autonomous dual-robot resection cooperation workflow is proposed and evaluated in this study. The integrated system achieves autonomous localization of the pseudotumor by tracking the near-infrared marker, and performs supervised autonomous resection in cadaver porcine tongues (N=3). The three pseudotumors were successfully removed from porcine samples. The evaluated average surface and depth resection errors are 1.19 and 1.83mm, respectively. This work is an essential step towards autonomous tumor resections.

Open-Heart Surgery at the Lagos State University Teaching Hospital commenced in 2004. Early years were based on a Cardiac Mission Model, but since 2017 the focus was on the transition to a Local Team Model with autonomous Open-Heart Surgery. The aim of this study is to describe our progress in making this transition, highlight lessons learned, and detail the outstanding challenges to be overcome. This study is a retrospective analysis of prospectively maintained data from the Lagos State University Teaching Hospital cardiothoracic database and Nigeria Open-Heart Surgery Registry between November 2004 and December 2021. Data extracted included patient demographics, EuroSCORE II, operative procedure, operative category, lead surgeon, complications, and outcomes. Over the study period, 100 operations were done over 2 time periods, 51 operations between 2004 and 2011 (Cardiac Mission Period) and 49 operations between 2017 and 2021 (Transition Period). In the Cardiac Mission Period, 21.6% of the operations were done by the Local Team and in the Transition Period this increased to 85.7% of the operations completed. Overall mortality was 14%, dropping from 17.6% in the Cardiac Mission Period to 10.2% in the Transition Period. The Local Team is now gradually taking on more diverse cases while striving to maintain good outcomes. Our institution has successfully made the transition from Cardiac Missions to Autonomous Open-Heart Surgery without an increase in mortality and a gradual increase in surgical volumes. Lessons learned included a strategy to focus on adult surgery, avoidance of high-risk cases, and moving from free surgery toward an appropriate cost structure for program sustainability. Contributory factors to the successful transition include the active support of the hospital management (provision of appropriate infrastructure and equipment, investment in training of the Local Team), continued humanitarian international collaborations focused on skill transfer, and maintenance of Local Team skills by collaborations with other active cardiac centers in Nigeria. Remaining challenges include financing to bridge equipment gaps, maintenance and replacement of equipment as well as the evolution of a national health insurance schema that would ideally support Open-Heart Surgery for Nigerian patients. Until that time, patients and programs must rely on supplemental funding of surgery to increase surgical volumes.

Retinal surgery is known to be a complicated and challenging task for an ophthalmologist even for retina specialists. Image guided robot-assisted intervention is among the novel and promising solutions that may enhance human capabilities during microsurgery. In this paper, a novel method is proposed for 3D navigation of a microsurgical instrument based on the projection of a spotlight during robot-assisted retinal surgery. To test the feasibility and effectiveness of the proposed method, a vessel tracking task in a phantom with a Remote Center of Motion (RCM) constraint is performed by the Steady-Hand Eye Robot (SHER). The results are compared to manual tracking, cooperative control tracking with the SHER and spotlight-based automatic tracking with SHER. The reported results are that the spotlight-based automatic tracking with SHER can reach an average tracking error of 0.013 mm and keeping distance error of 0.1 mm from the desired range demonstrating a significant improvement compared to manual or cooperative control methods alone.

METHODS: Cystoscopy is the gold standard for initial macroscopic assessments of the human urinary bladder to rule out (or diagnose) bladder cancer (BCa). Despite having guidelines, cystoscopic findings are diverse and often challenging to classify. The extent of the false negatives and false positives in cystoscopic diagnosis is currently unknown. We suspect that there is a certain degree of under-diagnosis (like the failure to detect malignant tumours) and over-diagnosis (e.g. sending the patient for unnecessary transurethral resection of bladder tumors with anesthesia) that put the patient at risk.
CONCLUSIONS: XAI robot-assisted cystoscopes would help to overcome the risks/flaws of conventional cystoscopy. Cystoscopy is considered a less life-threatening starting point for automation than open surgical procedures. Semi-autonomous cystoscopy requires standards and cystoscopy is a good procedure to establish a model that can then be exported/copied to other procedures of endoscopy and surgery. Standards also define the automation levels-an issue for medical product law. These cystoscopy skills do not give full autonomy to the machine, and represent a surgical parallel to \'Autonomous Driving\' (where a standard requires a human supervisor to remain in the \'vehicle\'). Here in robotic cystoscopy, a human supervisor remains bedside in the \'operating room\' as a \'human-in-the-loop\' in order to safeguard patients. The urologists will be able to delegate personal- and time-consuming cystoscopy to a specialised nurse. The result of automated diagnostic cystoscopy is a short video (with pre-processed photos from the video), which are then reviewed by the urologists at a more convenient time.

As a result of major advances in deep learning algorithms and computer processing power, there have been important developments in the fields of medicine and robotics. Although fully autonomous surgery systems where human impact will be minimized are still a long way off, systems with partial autonomy have gradually entered clinical use. In this review, articles on autonomous surgery classified and summarized, with the aim of informing the reader about questions such as \"What is autonomic surgery?\" and in which areas studies are progressing.

OBJECTIVE: The aim of the current narrative review was to summarize the available evidence in the literature on artificial intelligence (AI) methods that have been applied during robotic surgery.
METHODS: A narrative review of the literature was performed on MEDLINE/Pubmed and Scopus database on the topics of artificial intelligence, autonomous surgery, machine learning, robotic surgery, and surgical navigation, focusing on articles published between January 2015 and June 2019. All available evidences were analyzed and summarized herein after an interactive peer-review process of the panel.
METHODS: The preliminary results of the implementation of AI in clinical setting are encouraging. By providing a readout of the full telemetry and a sophisticated viewing console, robot-assisted surgery can be used to study and refine the application of AI in surgical practice. Machine learning approaches strengthen the feedback regarding surgical skills acquisition, efficiency of the surgical process, surgical guidance and prediction of postoperative outcomes. Tension-sensors on the robotic arms and the integration of augmented reality methods can help enhance the surgical experience and monitor organ movements.
CONCLUSIONS: The use of AI in robotic surgery is expected to have a significant impact on future surgical training as well as enhance the surgical experience during a procedure. Both aim to realize precision surgery and thus to increase the quality of the surgical care. Implementation of AI in master-slave robotic surgery may allow for the careful, step-by-step consideration of autonomous robotic surgery.