Robotic surgery of head and neck cancers, a narrative review

Research output of Iran over the past two years: Contributions from the European Journal of Translational Myology

Over the last two decades, all branches of science and technology have rapidly progressed, including the medical area. In Middle Eastern countries, including Iran, the increased growth of technology and science production has led to this rapid medical progress. Recent evidence has revealed that the increase in the number of scientific papers generated in Iran after the Iraqi imposed war against Iran has been eight times that in six main countries in the Middle East, including Egypt, Turkey, Jordan, Saudi Arabia, Iraq, and Syria. Although Iran’s output in sciences has been still small in comparison to developed countries, it has significantly grown in the last decade, which is hoped to continue to increase and improve. The current paper shows a portion of the science production in Iran over the past two years based on papers published in the European Journal of Translational Myology in 2020 and 2021. Although the Issue 32(1), 2022 in a special section contains several typescripts from Iran, they were not included in our analysis since they have not been yet indexed in the Scopus database. However, our report would encourage researchers of diverse nationalities to take part in research works and author joint papers. Further, this paper can be used to rate the universities and research centers regarding their science output, as well as being beneficial to university students, lecturers, and governments.

Supervised Autonomous Electrosurgery for Soft Tissue Resection

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.

Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?

Gliomas are one of the most devastating primary brain tumors which impose significant management challenges to the clinicians. The aggressive behaviour of gliomas is mainly attributed to their rapid proliferation, unravelled genomics and the blood-brain barrier which protects the tumor cells from chemotherapeutic regimens. Suspects of brain tumors are usually assessed by magnetic resonance imaging and computed tomography. These images allow surgeons to decide on the tumor grading, intra-operative pathology, feasibility of surgery, and treatment planning. All these data are compiled manually by physicians, wherein it takes time for the validation of results and concluding the treatment modality. In this context, the arrival of artificial intelligence in this era of personalized medicine, has proven promising performance in the diagnosis and management of gliomas. Starting from grading prediction till outcome evaluation, artificial intelligence-based forefronts have revolutionized oncological research. Interestingly, this approach has also been able to precisely differentiate tumor lesion from healthy tissues. However, till date, their utility in neuro-oncological field remains limited due to the issues pertaining to their reliability and transparency. Hence, to shed novel insights on the "clinical utility of this novel approach on glioma management" and to reveal "the black-boxes that have to be solved for fruitful application of artificial intelligence in neuro-oncology research", we provide in this review, a succinct description of the potential gear of artificial intelligence-based avenues in glioma treatment and the barriers that impede their rapid implementation in neuro-oncology.