Application of Surgical Safety Standards to Robotic Surgery: Five Principles of Ethics for Nonmaleficence

A comprehensive survey on the use of deep learning techniques in glioblastoma

Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene expression, driven by alterations in epigenetic mechanisms. The integration of artificial intelligence, inclusive of machine learning algorithms, has emerged as an indispensable asset in medical analyses. AI is becoming a necessary tool in medicine and beyond. Current research on Glioblastoma predominantly revolves around non-omics data modalities, prominently including magnetic resonance imaging, computed tomography, and positron emission tomography. Nonetheless, the assimilation of omic data-encompassing gene expression through transcriptomics and epigenomics-offers pivotal insights into patients' conditions. These insights, reciprocally, hold significant value in refining diagnoses, guiding decision- making processes, and devising efficacious treatment strategies. This survey's core objective encompasses a comprehensive exploration of noteworthy applications of machine learning methodologies in the domain of glioblastoma, alongside closely associated research pursuits. The study accentuates the deployment of artificial intelligence techniques for both non-omics and omics data, encompassing a range of tasks. Furthermore, the survey underscores the intricate challenges posed by the inherent heterogeneity of Glioblastoma, delving into strategies aimed at addressing its multifaceted nature.

Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding-An Interdisciplinary Project

Artificial intelligence (AI) is gaining increasing interest in the field of medicine because of its capacity to process big data and pattern recognition. Cardiotocography (CTG) is widely used for the assessment of foetal well-being and uterine contractions during pregnancy and labour. It is characterised by inter- and intraobserver variability in interpretation, which depends on the observers' experience. Artificial intelligence (AI)-assisted interpretation could improve its quality and, thus, intrapartal care. Cardiotocography (CTG) raw signals from labouring women were extracted from the database at the University Hospital of Bern between 2006 and 2019. Later, they were matched with the corresponding foetal outcomes, namely arterial umbilical cord pH and 5-min APGAR score. Excluded were deliveries where data were incomplete, as well as multiple births. Clinical data were grouped regarding foetal pH and APGAR score at 5 min after delivery. Physiological foetal pH was defined as 7.15 and above, and a 5-min APGAR score was considered physiologic when reaching ≥7. With these groups, the algorithm was trained to predict foetal hypoxia. Raw data from 19,399 CTG recordings could be exported. This was accomplished by manually searching the patient's identification numbers (PIDs) and extracting the corresponding raw data from each episode. For some patients, only one episode per pregnancy could be found, whereas for others, up to ten episodes were available. Initially, 3400 corresponding clinical outcomes were found for the 19,399 CTGs (17.52%). Due to the small size, this dataset was rejected, and a new search strategy was elaborated. After further matching and curation, 6141 (31.65%) paired data samples could be extracted (cardiotocography raw data and corresponding maternal and foetal outcomes). Of these, half will be used to train artificial intelligence (AI) algorithms, whereas the other half will be used for analysis of efficacy. Complete data could only be found for one-third of the available population. Yet, to our knowledge, this is the most exhaustive and second-largest cardiotocography database worldwide, which can be used for computer analysis and programming. A further enrichment of the database is planned.

Telemedicine and artificial intelligence to support self-isolation of COVID-19 patients: Recent updates and challenges

Background

Asymptomatic and high-risk COVID-19 patients are advised to self-isolate at home. However, patients may not realize that the condition is deteriorating until too late.

Objective

This study aims to review various artificial intelligence-based telemedicine research during the COVID-19 outbreak and proposes a framework for developing telemedicine powered by artificial intelligence to monitor progression in COVID-19 patients during isolation at home. It also aims to map challenges using artificial intelligence-based telemedicine in the community.

Methods

A systematic review was performed for the related articles published in 2019-2021 and conducted in the PubMed and ScienceDirect database using the keywords "telemedicine," "artificial intelligence," and "COVID-19". The inclusion criteria were full-text articles and original research written in the English language.

Results

Thirteen articles were included in this review to describe the current application of artificial intelligence-based telemedicine during the COVID-19 pandemic. Various current applications have been implemented, such as for early diagnosis and tracing of contact for the users, to monitor symptoms and decision-making treatment, clinical management, and virtual and remote treatment. We also proposed the framework of telemedicine powered by artificial intelligence for support the self-isolation of COVID-19 patients based on the recent update in technology. However, we identified some challenges for using digital health technologies because of the ethical and practical use, the policy and regulation, and device use both for healthcare workers and patients.

Conclusion

Artificial intelligence promises to improve the practice of medicine in various ways. However, practical applications still need to be explored, and medical professionals also need to adapt to these advances for better healthcare delivery to the public.

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

Clinical Desire for an Artificial Intelligence-Based Surgical Assistant System: Electronic Survey-Based Study

Background

Techniques utilizing artificial intelligence (AI) are rapidly growing in medical research and development, especially in the operating room. However, the application of AI in the operating room has been limited to small tasks or software, such as clinical decision systems. It still largely depends on human resources and technology involving the surgeons’ hands. Therefore, we conceptualized AI-based solo surgery (AISS) defined as laparoscopic surgery conducted by only one surgeon with support from an AI-based surgical assistant system, and we performed an electronic survey on the clinical desire for such a system.

Objective

This study aimed to evaluate the experiences of surgeons who have performed laparoscopic surgery, the limitations of conventional laparoscopic surgical systems, and the desire for an AI-based surgical assistant system for AISS.

Methods

We performed an online survey for gynecologists, urologists, and general surgeons from June to August 2017. The questionnaire consisted of six items about experience, two about limitations, and five about the clinical desire for an AI-based surgical assistant system for AISS.

Results

A total of 508 surgeons who have performed laparoscopic surgery responded to the survey. Most of the surgeons needed two or more assistants during laparoscopic surgery, and the rate was higher among gynecologists (251/278, 90.3%) than among general surgeons (123/173, 71.1%) and urologists (35/57, 61.4%). The majority of responders answered that the skillfulness of surgical assistants was “very important” or “important.” The most uncomfortable aspect of laparoscopic surgery was unskilled movement of the camera (431/508, 84.8%) and instruments (303/508, 59.6%). About 40% (199/508, 39.1%) of responders answered that the AI-based surgical assistant system could substitute 41%-60% of the current workforce, and 83.3% (423/508) showed willingness to buy the system. Furthermore, the most reasonable price was US $30,000-50,000.

Conclusions

Surgeons who perform laparoscopic surgery may feel discomfort with the conventional laparoscopic surgical system in terms of assistant skillfulness, and they may think that the skillfulness of surgical assistants is essential. They desire to alleviate present inconveniences with the conventional laparoscopic surgical system and to perform a safe and comfortable operation by using an AI-based surgical assistant system for AISS.

Toward a fully robotic surgery: Performing robotic major liver resection with no table-side surgeon

BACKGROUND

Evidence has suggested that robotic system helps perform more major liver resections. However, the required table-side surgeon has remained a concern because of the uncertain performance and the incomplete control of console surgeon.

METHODS

Data were reviewed for consecutive 333 robotic liver resections, of which 56 patients underwent left liver resection with the usual setting, and 35 with no table-side surgeon.

RESULTS

No conversion was required in the setting with no table-side surgeon. The group without the table-side surgeon had similar complication rates, blood loss, and operative time compared with that of the normal settings, as well as focused analysis on major left hemihepatectomy.

CONCLUSION

Our data suggest that performing robotic major liver resection without the presence of the table-side surgeon is safe and feasible. The concise performance of robotic platforms might accelerate the machine learning process along with the ability to predict patterns of future autonomous surgery.

Artificial intelligence and its potential in oncology

The two main branches associated with Artificial Intelligence (AI) in medicine are virtual and physical. The virtual component includes machine learning (ML) and algorithms, whereas physical AI includes medical devices and robots for delivering care. AI is used successfully in tumour segmentation, histopathological diagnosis, tracking tumour development, and prognosis prediction. CURATE.AI, developed at the National University of Singapore, is a platform that automatically decides the optimum dose of drugs for a durable response, allowing the patient to resume a completely normal life. With the involvement of technology multinationals, such as Google and Microsoft, in AI and healthcare in association with leading healthcare companies, the future of AI in healthcare looks very promising.

Joint consensus on abdominal robotic surgery and anesthesia from a task force of the SIAARTI and SIC

Minimally invasive surgical procedures have revolutionized the world of surgery in the past decades. While laparoscopy, the first minimally invasive surgical technique to be developed, is widely used and has been addressed by several guidelines and recommendations, the implementation of robotic-assisted surgery is still hindered by the lack of consensus documents that support healthcare professionals in the management of this novel surgical procedure. Here we summarize the available evidence and provide expert opinion aimed at improving the implementation and resolution of issues derived from robotic abdominal surgery procedures. A joint task force of Italian surgeons, anesthesiologists and clinical epidemiologists reviewed the available evidence on robotic abdominal surgery. Recommendations were graded according to the strength of evidence. Statements and recommendations are provided for general issues regarding robotic abdominal surgery, operating theatre organization, preoperative patient assessment and preparation, intraoperative management, and postoperative procedures and discharge. The consensus document provides evidence-based recommendations and expert statements aimed at improving the implementation and management of robotic abdominal surgery.

Single-site robotic-assisted laparoscopic cholecystectomy in children and adolescents: a report of 20 cases

BACKGROUND

Single-site laparoscopy is increasingly popular for straightforward operations like appendectomy. Due to limited triangulation and maneuverability, single-site cholecystectomy is riskier and more difficult. Robotics offer to make it easier and safer.

METHODS

Twenty children and adolescents underwent robotic-assisted single-site cholecystectomy at a large academic children's hospital. Patients were not randomized; patients were offered the option of robotic-assisted single-site (SSR) or standard four-incision laparoscopic (LAP) cholecystectomy. Demographics and perioperative details were compared with those of a comparable cohort who underwent LAP during the same period.

RESULTS

The two groups were similar in physical characteristics and indications for operation. The robotic operations took longer but both groups received similar PRN doses of parenteral opiates. Patients in the SSR group were all discharged on the first postoperative day. There were no major complications in either group but a slightly higher incidence of minor wound complications in the SSR group.

CONCLUSION

Robotic-assisted single-site cholecystectomy appears to be a safe alternative to standard laparoscopy with a similar postoperative pain profile, short postoperative lengths of stay, and, for some, a superior cosmetic result. Nevertheless, it comes with longer set-up and operative times, a higher incidence of minor wound complications, an unknown but possibly higher risk of incisional hernia, and higher costs.

Artificial intelligence in medicine

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.