Artificial intelligence-based computer vision in surgery: Recent advances and future perspectives

Training and assessing convolutional neural network performance in automatic vascular segmentation using Ga-68 DOTATATE PET/CT

To evaluate a convolutional neural network's performance (nnU-Net) in the assessment of vascular contours, calcification and PET tracer activity using Ga-68 DOTATATE PET/CT. Patients who underwent Ga-68 DOTATATE PET/CT imaging over a 12-month period for neuroendocrine investigation were included. Manual cardiac and aortic segmentations were performed by an experienced observer. Scans were randomly allocated in ratio 64:16:20 for training, validation and testing of the nnU-Net model. PET tracer uptake and calcium scoring were compared between segmentation methods and different observers. 116 patients (53.5% female) with a median age of 64.5 years (range 23-79) were included. There were strong, positive correlations between all segmentations (mostly r > 0.98). There were no significant differences between manual and AI segmentation of SUVmean for global cardiac (mean ± SD 0.71 ± 0.22 vs. 0.71 ± 0.22; mean diff 0.001 ± 0.008, p > 0.05), ascending aorta (mean ± SD 0.44 ± 0.14 vs. 0.44 ± 0.14; mean diff 0.002 ± 0.01, p > 0.05), aortic arch (mean ± SD 0.44 ± 0.10 vs. 0.43 ± 0.10; mean diff 0.008 ± 0.16, p > 0.05) and descending aorta (mean ± SD < 0.001; 0.58 ± 0.12 vs. 0.57 ± 0.12; mean diff 0.01 ± 0.03, p > 0.05) contours. There was excellent agreement between the majority of manual and AI segmentation measures (r ≥ 0.80) and in all vascular contour calcium scores. Compared with the manual segmentation approach, the CNN required a significantly lower workflow time. AI segmentation of vascular contours using nnU-Net resulted in very similar measures of PET tracer uptake and vascular calcification when compared to an experienced observer and significantly reduced workflow time.

Robotic performance metrics model fellow proficiency in living donor nephrectomy

We investigated the use of robotic objective performance metrics (OPM) to predict number of cases to proficiency and independence among abdominal transplant fellows performing robot-assisted donor nephrectomy (RDN). 101 RDNs were performed by 5 transplant fellows from September 2020 to October 2023. OPM included fellow percent active control time (%ACT) and handoff counts (HC). Proficiency was defined as ACT ≥ 80% and HC ≤ 2, and independence as ACT ≥ 99% and HC ≤ 1. Case number was significantly associated with increasing fellow %ACT, with proficiency estimated at 14 cases and independence at 32 cases (R2 = 0.56, p < 0.001). Similarly, case number was significantly associated with decreasing HC, with proficiency at 18 cases and independence at 33 cases (R2 = 0.29, p < 0.001). Case number was not associated with total active console time (p = 0.91). Patient demographics, operative characteristics, and outcomes were not associated with OPM, except for donor estimated blood loss (EBL), which positively correlated with HC. Abdominal transplant fellows demonstrated proficiency at 14-18 cases and independence at 32-33 cases. Total active console time remained unchanged, suggesting that increasing fellow autonomy does not impede operative efficiency. These findings may serve as a benchmark for training abdominal transplant surgery fellows independently and safely in RDN.

Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer

Gastric cancer remains a formidable health challenge worldwide; early detection and effective surgical intervention are critical for improving patient outcomes. This comprehensive review explores the evolving landscape of gastric cancer management, emphasizing the significant contributions of artificial intelligence (AI) in revolutionizing both diagnostic and therapeutic approaches. Despite advancements in the medical field, the subtle nature of early gastric cancer symptoms often leads to late-stage diagnoses, where survival rates are notably decreased. Historically, the treatment of gastric cancer has transitioned from palliative care to surgical resection, evolving further with the introduction of minimally invasive surgical (MIS) techniques. In the current era, AI has emerged as a transformative force, enhancing the precision of early gastric cancer detection through sophisticated image analysis, and supporting surgical decision-making with predictive modeling and real-time preop-, intraop-, and postoperative guidance. However, the deployment of AI in healthcare raises significant ethical, legal, and practical challenges, including the necessity for ongoing professional education and the development of standardized protocols to ensure patient safety and the effective use of AI technologies. Future directions point toward a synergistic integration of AI with clinical best practices, promising a new era of personalized, efficient, and safer gastric cancer management.

Improving Surgical Scene Semantic Segmentation through a Deep Learning Architecture with Attention to Class Imbalance

This article addresses the semantic segmentation of laparoscopic surgery images, placing special emphasis on the segmentation of structures with a smaller number of observations. As a result of this study, adjustment parameters are proposed for deep neural network architectures, enabling a robust segmentation of all structures in the surgical scene. The U-Net architecture with five encoder-decoders (U-Net5ed), SegNet-VGG19, and DeepLabv3+ employing different backbones are implemented. Three main experiments are conducted, working with Rectified Linear Unit (ReLU), Gaussian Error Linear Unit (GELU), and Swish activation functions. The applied loss functions include Cross Entropy (CE), Focal Loss (FL), Tversky Loss (TL), Dice Loss (DiL), Cross Entropy Dice Loss (CEDL), and Cross Entropy Tversky Loss (CETL). The performance of Stochastic Gradient Descent with momentum (SGDM) and Adaptive Moment Estimation (Adam) optimizers is compared. It is qualitatively and quantitatively confirmed that DeepLabv3+ and U-Net5ed architectures yield the best results. The DeepLabv3+ architecture with the ResNet-50 backbone, Swish activation function, and CETL loss function reports a Mean Accuracy (MAcc) of 0.976 and Mean Intersection over Union (MIoU) of 0.977. The semantic segmentation of structures with a smaller number of observations, such as the hepatic vein, cystic duct, Liver Ligament, and blood, verifies that the obtained results are very competitive and promising compared to the consulted literature. The proposed selected parameters were validated in the YOLOv9 architecture, which showed an improvement in semantic segmentation compared to the results obtained with the original architecture.

Robust tracking of deformable anatomical structures with severe occlusions using deformable geometrical primitives

BACKGROUND AND OBJECTIVE

Surgical robotics tends to develop cognitive control architectures to provide certain degree of autonomy to improve patient safety and surgery outcomes, while decreasing the required surgeons' cognitive load dedicated to low level decisions. Cognition needs workspace perception, which is an essential step towards automatic decision-making and task planning capabilities. Robust and accurate detection and tracking in minimally invasive surgery suffers from limited visibility, occlusions, anatomy deformations and camera movements.

METHOD

This paper develops a robust methodology to detect and track anatomical structures in real time to be used in automatic control of robotic systems and augmented reality. The work focuses on the experimental validation in highly challenging surgery: fetoscopic repair of Open Spina Bifida. The proposed method is based on two sequential steps: first, selection of relevant points (contour) using a Convolutional Neural Network and, second, reconstruction of the anatomical shape by means of deformable geometric primitives.

RESULTS

The methodology performance was validated with different scenarios. Synthetic scenario tests, designed for extreme validation conditions, demonstrate the safety margin offered by the methodology with respect to the nominal conditions during surgery. Real scenario experiments have demonstrated the validity of the method in terms of accuracy, robustness and computational efficiency.

CONCLUSIONS

This paper presents a robust anatomical structure detection in present of abrupt camera movements, severe occlusions and deformations. Even though the paper focuses on a case study, Open Spina Bifida, the methodology is applicable in all anatomies which contours can be approximated by geometric primitives. The methodology is designed to provide effective inputs to cognitive robotic control and augmented reality systems that require accurate tracking of sensitive anatomies.

Advancements and Innovations in the Surgical Management of Sacrococcygeal Pilonidal Sinus: A Comprehensive Review

Sacrococcygeal pilonidal sinus (SPS) is a common condition characterized by the formation of a sinus tract or cavity in the sacrococcygeal region, often containing hair and debris. Surgical management plays a crucial role in its treatment due to its chronic and recurrent nature. This comprehensive review explores the advancements and innovations in the surgical management of SPS. The review begins with an overview of the historical perspective, anatomy, and pathophysiology of the condition, followed by a discussion of current surgical techniques, including conventional excision, flap procedures, and minimally invasive approaches. Recent advancements, such as laser therapy, radiological guidance techniques, and robotic-assisted surgery, are also examined. The key findings from outcomes research are summarized, including postoperative pain management, recurrence rates, and patient satisfaction. The implications for clinical practice are discussed, emphasizing the importance of staying updated on the latest surgical techniques and adopting a personalized approach to treatment. Recommendations for future research are provided, highlighting the need for prospective studies comparing different surgical techniques, as well as research focusing on minimally invasive approaches and predictive models for recurrence risk. Collaboration among researchers, clinicians, and industry partners is essential to drive innovation and improve outcomes for patients with SPS.

An Automated Video Analysis System for Retrospective Assessment and Real-Time Monitoring of Endoscopic Procedures (with Video)

BACKGROUND AND AIMS

Accurate recognition of endoscopic instruments facilitates quantitative evaluation and quality control of endoscopic procedures. However, no relevant research has been reported. In this study, we aimed to develop a computer-assisted system, EndoAdd, for automated endoscopic surgical video analysis based on our dataset of endoscopic instrument images.

METHODS

Large training and validation datasets containing 45,143 images of 10 different endoscopic instruments and a test dataset of 18,375 images collected from several medical centers were used in this research. Annotated image frames were used to train the state-of-the-art object detection model, YOLO-v5, to identify the instruments. Based on the frame-level prediction results, we further developed a hidden Markov model to perform video analysis and generate heatmaps to summarize the videos.

RESULTS

EndoAdd achieved high accuracy (>97%) on the test dataset for all 10 endoscopic instrument types. The mean average accuracy, precision, recall, and F1-score were 99.1%, 92.0%, 88.8%, and 89.3%, respectively. The area under the curve values exceeded 0.94 for all instrument types. Heatmaps of endoscopic procedures were generated for both retrospective and real-time analyses.

CONCLUSIONS

We successfully developed an automated endoscopic video analysis system, EndoAdd, which supports retrospective assessment and real-time monitoring. It can be used for data analysis and quality control of endoscopic procedures in clinical practice.

Technical and ethical considerations in telesurgery

Telesurgery, a cutting-edge field at the intersection of medicine and technology, holds immense promise for enhancing surgical capabilities, extending medical care, and improving patient outcomes. In this scenario, this article explores the landscape of technical and ethical considerations that highlight the advancement and adoption of telesurgery. Network considerations are crucial for ensuring seamless and low-latency communication between remote surgeons and robotic systems, while technical challenges encompass system reliability, latency reduction, and the integration of emerging technologies like artificial intelligence and 5G networks. Therefore, this article also explores the critical role of network infrastructure, highlighting the necessity for low-latency, high-bandwidth, secure and private connections to ensure patient safety and surgical precision. Moreover, ethical considerations in telesurgery include patient consent, data security, and the potential for remote surgical interventions to distance surgeons from their patients. Legal and regulatory frameworks require refinement to accommodate the unique aspects of telesurgery, including liability, licensure, and reimbursement. Our article presents a comprehensive analysis of the current state of telesurgery technology and its potential while critically examining the challenges that must be navigated for its widespread adoption.

Can artificial intelligence help decision-making in arthroscopy? Part 1: Use of a standardized analysis protocol improves inter-observer agreement of arthroscopic diagnostic assessments of the long head of biceps tendon in small rotator cuff tears

INTRODUCTION

Injuries of the long head of biceps (LHB) tendon are common but difficult to diagnose clinically or using imaging. Arthroscopy is the preferred means of diagnostic assessment of the LHB, but it often proves challenging. Its reliability and reproducibility have not yet been assessed. Artificial intelligence (AI) could assist in the arthroscopic analysis of the LHB. The main objective of this study was to evaluate the inter-observer agreement for the specific LHB assessment, according to an analysis protocol based on images of interest. The secondary objective was to define a video database, called "ground truth", intended to create and train AI for the LHB assessment.

HYPOTHESIS

The hypothesis was that the inter-observer agreement analysis, on standardized images, was strong enough to allow the "ground truth" videos to be used as an input database for an AI solution to be used in making arthroscopic LHB diagnoses.

MATERIALS AND METHOD

One hundred and ninety-nine sets of standardized arthroscopic images of LHB exploration were evaluated by 3 independent observers. Each had to characterize the healthy or pathological state of the tendon, specifying the type of lesion: partial tear, hourglass hypertrophy, instability, fissure, superior labral anterior posterior lesion (SLAP 2), chondral print and pathological pulley without instability. Inter-observer agreement levels were measured using Cohen's Kappa (K) coefficient and Kappa Accuracy.

RESULTS

The strength of agreement was moderate to strong according to the observers (Kappa 0.54 to 0.7 and KappaAcc from 86 to 92%), when determining the healthy or pathological state of the LHB. When the tendon was pathological, the strength of agreement was moderate to strong when it came to a partial tear (Kappa 0.49 to 0.71 and KappaAcc from 85 to 92%), fissure (Kappa -0.5 to 0.7 and KappaAcc from 36 to 93%) or a SLAP tear (0.54 to 0.88 and KappaAcc from 90 to 97%). It was low for unstable lesion (Kappa 0.04 to 0.25 and KappaAcc from 36 to 88%).

CONCLUSION

The analysis of the LHB, from arthroscopic images, had a high level of agreement for the diagnosis of its healthy or pathological nature. However, the agreement rate decreased for the diagnosis of rare or dynamic tendon lesions. Thus, AI engineered from human analysis would have the same difficulties if it was limited only to an arthroscopic analysis. The integration of clinical and paraclinical data is necessary to improve the arthroscopic diagnosis of LHB injuries. It also seems to be an essential prerequisite for making a so-called "ground truth" database for building a high-performance AI solution.

LEVEL OF EVIDENCE

III; inter-observer prospective series.

Can artificial intelligence help decision-making in arthroscopy? Part 2: The IA-RTRHO model - a decision-making aid for long head of the biceps diagnoses in small rotator cuff tears

INTRODUCTION

The possible applications of artificial intelligence (AI) in orthopedic surgery are promising. Deep learning can be utilized in arthroscopic surgery due to the video signal used by computer vision. The intraoperative management of the long head of biceps (LHB) tendon is the subject of a long-standing controversy. The main objective of this study was to model a diagnostic AI capable of determining the healthy or pathological state of the LHB on arthroscopic images. The secondary objective was to create a second diagnostic AI model based on arthroscopic images and the medical, clinical and imaging data of each patient, to determine the healthy or pathological state of the LHB.

HYPOTHESIS

The hypothesis of this study was that it was possible to construct an AI model from operative arthroscopic images to aid in the diagnosis of the healthy or pathological state of the LHB, and its analysis would be superior to a human analysis.

MATERIALS AND METHODS

Prospective clinical and imaging data from 199 patients were collected and associated with images from a validated protocoled arthroscopic video analysis, called "ground truth", made by the operating surgeon. A model based on a convolutional neural network (CNN) modeled via transfer learning on the Inception V3 model was built for the analysis of arthroscopic images. This model was then coupled to MultiLayer Perceptron (MLP), integrating clinical and imaging data. Each model was trained and tested using supervised learning.

RESULTS

The accuracy of the CNN in diagnosing the healthy or pathological state of the LHB was 93.7% in learning and 80.66% in generalization. Coupled with the clinical data of each patient, the accuracy of the model assembling the CNN and MLP were respectively 77% and 58% in learning and in generalization.

CONCLUSION

The AI model built from a CNN manages to determine the healthy or pathological state of the LHB with an accuracy rate of 80.66%. An increase in input data to limit overfitting, and the automation of the detection phase by a Mask-R-CNN are ways of improving the model. This study is the first to assess the ability of an AI to analyze arthroscopic images, and its results need to be confirmed by further studies on this subject.

LEVEL OF EVIDENCE

III Diagnostic study.

The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review

Applications of machine learning in the healthcare field have become increasingly diverse. In this review, we investigated the integration of artificial intelligence (AI) in predicting the prognosis of patients with central nervous system disorders such as stroke, traumatic brain injury, and spinal cord injury. AI algorithms have shown promise in prognostic assessment, but challenges remain in achieving a higher prediction accuracy for practical clinical use. We suggest that accumulating more diverse data, including medical imaging and collaborative efforts among hospitals, can enhance the predictive capabilities of AI. As healthcare professionals become more familiar with AI, its role in central nervous system rehabilitation is expected to advance significantly, revolutionizing patient care.

Appearance of the Bowel and Mesentery During Surgery Is Not Predictive of Postoperative Recurrence After Ileocecal Resection for Crohn's Disease: A Prospective Monocentric Study

BACKGROUND

Very few risk factors for postoperative recurrence (POR) of Crohn's Disease (CD) after ileocecal resection have been identified. The aim of the present study was to verify the association between an a priori defined list of intraoperative macroscopic findings and POR.

METHODS

This was a prospective observational study including patients undergoing primary ileocecal resection for CD. Four intraoperative factors were independently evaluated by 2 surgeons: length of resected ileum, mesentery thickness, presence of areas of serosal fat infiltration, or abnormal serosal vasodilation on normal bowel proximal to the resected bowel. The primary end point was early endoscopic POR at month 6 and defined as modified Rutgeerts score ≥i2b. Secondary end points were clinical and surgical recurrence.

RESULTS

Between September 2020 and November 2022, 83 consecutive patients were included. Early endoscopic recurrence occurred in 45 of 76 patients (59.2%). Clinical and biochemical recurrence occurred in 17.3% (95% confidence interval, [CI], 10.4%-28.0%) and 14.6% of the patients after 12 months. The risk of developing endoscopic and clinical recurrence was 1.127 (95% CI, 0.448;2.834, P = .799) and 0.896 (95% CI, 0.324-2.478, P = .832) when serosal fat infiltration was observed, and 1.388 (95% CI, 0.554-3.476, P = .484), and 1.153 (95% CI, 0.417;3.187, P = .783) when abnormal serosal vasodilation was observed. Similarly, length of the resected bowel and mesentery thickness showed no association with POR. A subgroup analysis on patients who received no postoperative medical prophylaxis did not identify any risk factor for endoscopic POR.

CONCLUSIONS

The macroscopic appearance of the bowel and associated mesentery during surgery does not seem to be predictive of POR after ileocecal resection for CD.

Artificial intelligence for the recognition of key anatomical structures in laparoscopic colorectal surgery

To prevent intraoperative organ injury, surgeons strive to identify anatomical structures as early and accurately as possible during surgery. The objective of this prospective observational study was to develop artificial intelligence (AI)-based real-time automatic organ recognition models in laparoscopic surgery and to compare its performance with that of surgeons. The time taken to recognize target anatomy between AI and both expert and novice surgeons was compared. The AI models demonstrated faster recognition of target anatomy than surgeons, especially novice surgeons. These findings suggest that AI has the potential to compensate for the skill and experience gap between surgeons.

Application of three-dimensional reconstruction technology in dentistry: a narrative review

BACKGROUND

Three-dimensional(3D) reconstruction technology is a method of transforming real goals into mathematical models consistent with computer logic expressions and has been widely used in dentistry, but the lack of review and summary leads to confusion and misinterpretation of information. The purpose of this review is to provide the first comprehensive link and scientific analysis of 3D reconstruction technology and dentistry to bridge the information bias between these two disciplines.

METHODS

The IEEE Xplore and PubMed databases were used for rigorous searches based on specific inclusion and exclusion criteria, supplemented by Google Academic as a complementary tool to retrieve all literature up to February 2023. We conducted a narrative review focusing on the empirical findings of the application of 3D reconstruction technology to dentistry.

RESULTS

We classify the technologies applied to dentistry according to their principles and summarize the different characteristics of each category, as well as the different application scenarios determined by these characteristics of each technique. In addition, we indicate their development prospects and worthy research directions in the field of dentistry, from individual techniques to the overall discipline of 3D reconstruction technology, respectively.

CONCLUSIONS

Researchers and clinicians should make different decisions on the choice of 3D reconstruction technology based on different objectives. The main trend in the future development of 3D reconstruction technology is the joint application of technology.

Development of a deep learning model for safe direct optical trocar insertion in minimally invasive surgery: an innovative method to prevent trocar injuries

BACKGROUND

Direct optical trocar insertion is a common procedure in laparoscopic minimally invasive surgery. However, misinterpretations of the abdominal wall anatomy can lead to severe complications. Artificial intelligence has shown promise in surgical endoscopy, particularly in the employment of deep learning models for anatomical landmark identification. This study aimed to integrate a deep learning model with an alarm system algorithm for the precise detection of abdominal wall layers during trocar placement.

METHOD

Annotated bounding boxes and assigned classes were based on the six layers of the abdominal wall: subcutaneous, anterior rectus sheath, rectus muscle, posterior rectus sheath, peritoneum, and abdominal cavity. The cutting-edge YOLOv8 model was combined with a deep learning detector to train the dataset. The model was trained on still images and inferenced on laparoscopic videos to ensure real-time detection in the operating room. The alarm system was activated upon recognizing the peritoneum and abdominal cavity layers. We assessed the model's performance using mean average precision (mAP), precision, and recall metrics.

RESULTS

A total of 3600 images were captured from 89 laparoscopic video cases. The proposed model was trained on 3000 images, validated with a set of 200 images, and tested on a separate set of 400 images. The results from the test set were 95.8% mAP, 89.8% precision, and 91.7% recall. The alarm system was validated and accepted by experienced surgeons at our institute.

CONCLUSION

We demonstrated that deep learning has the potential to assist surgeons during direct optical trocar insertion. During trocar insertion, the proposed model promptly detects precise landmark references in real-time. The integration of this model with the alarm system enables timely reminders for surgeons to tilt the scope accordingly. Consequently, the implementation of the framework provides the potential to mitigate complications associated with direct optical trocar placement, thereby enhancing surgical safety and outcomes.

Artificial Intelligence in Surgery and Its Potential for Gastric Cancer

Artificial intelligence (AI) has made significant progress in recent years, and many medical fields are attempting to introduce AI technology into clinical practice. Currently, much research is being conducted to evaluate that AI can be incorporated into surgical procedures to make them safer and more efficient, subsequently to obtain better outcomes for patients. In this paper, we review basic AI research regarding surgery and discuss the potential for implementing AI technology in gastric cancer surgery. At present, research and development is focused on AI technologies that assist the surgeon's understandings and judgment during surgery, such as anatomical navigation. AI systems are also being developed to recognize in which the surgical phase is ongoing. Such a surgical phase recognition systems is considered for effective storage of surgical videos and education, in the future, for use in systems to objectively evaluate the skill of surgeons. At this time, it is not considered practical to let AI make intraoperative decisions or move forceps automatically from an ethical standpoint, too. At present, AI research on surgery has various limitations, and it is desirable to develop practical systems that will truly benefit clinical practice in the future.

The future of artificial intelligence in neurosurgery: A narrative review

Background:

Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds.

Methods:

A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research.

Results:

The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models.

Conclusion:

Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients.

Robotics in laparoscopic surgery - A review

Because of the increasing use of laparoscopic surgeries, robotic technologies have been developed to overcome the challenges these surgeries impose on surgeons. This paper presents an overview of the current state of surgical robots used in laparoscopic surgeries. Four main categories were discussed: handheld laparoscopic devices, laparoscope positioning robots, master–slave teleoperated systems with dedicated consoles, and robotic training systems. A generalized control block diagram is developed to demonstrate the general control scheme for each category of surgical robots. In order to review these robotic technologies, related published works were investigated and discussed. Detailed discussions and comparison tables are presented to compare their effectiveness in laparoscopic surgeries. Each of these technologies has proved to be beneficial in laparoscopic surgeries.

Concepts and Trends n Autonomy for Robot-Assisted Surgery

Surgical robots have been widely adopted with over 4000 robots being used in practice daily. However, these are telerobots that are fully controlled by skilled human surgeons. Introducing "surgeon-assist"-some forms of autonomy-has the potential to reduce tedium and increase consistency, analogous to driver-assist functions for lanekeeping, cruise control, and parking. This article examines the scientific and technical backgrounds of robotic autonomy in surgery and some ethical, social, and legal implications. We describe several autonomous surgical tasks that have been automated in laboratory settings, and research concepts and trends.

The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature

This is a review focused on advances and current limitations of computer vision (CV) and how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to one that we previously published in Sensors entitled, “Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?” As opposed to that article that also discussed issues of machine learning, deep learning and natural language processing, this review will delve deeper into the field of CV. Additionally, non-visual forms of data that can aid computerized robots in the performance of more autonomous actions, such as instrument priors and audio haptics, will also be highlighted. Furthermore, the current existential crisis for surgeons, endoscopists and interventional radiologists regarding more autonomy during procedures will be discussed. In summary, this paper will discuss how to harness the power of CV to keep doctors who do interventions in the loop.

Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives

Background

Machine learning (ML) has been successful in several fields of healthcare, however the use of ML within bariatric surgery seems to be limited. In this systematic review, an overview of ML applications within bariatric surgery is provided.

Methods

The databases PubMed, EMBASE, Cochrane, and Web of Science were searched for articles describing ML in bariatric surgery. The Cochrane risk of bias tool and the PROBAST tool were used to evaluate the methodological quality of included studies.

Results

The majority of applied ML algorithms predicted postoperative complications and weight loss with accuracies up to 98%.

Conclusions

In conclusion, ML algorithms have shown promising capabilities in the prediction of surgical outcomes after bariatric surgery. Nevertheless, the clinical introduction of ML is dependent upon the external validation of ML.