Computer vision in surgery

Ward, Thomas M.; Mascagni, Pietro; Ban, Yutong; Rosman, Guy; Padoy, Nicolas; Meireles, Ozanan R.; Hashimoto, Daniel A.

doi:10.1016/j.surg.2020.10.039

Cited by 102 publications

(65 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These approaches could have potential utility in robotic systems if they can assist in automatic camera focus, augment force application, or provide support in complication management. 46,47 Lastly, current limitations with many augmented realities and neuronavigation setups include inaccuracy and interruption of workflow, which an integrated robotic setup may aid in alleviating.…”

Section: Future Directionsmentioning

confidence: 99%

Robotic and robot-assisted skull base neurosurgery: systematic review of current applications and future directions

Pangal

Coté

Ruzevick

et al. 2022

Neurosurgical Focus

View full text Add to dashboard Cite

OBJECTIVE The utility of robotic instrumentation is expanding in neurosurgery. Despite this, successful examples of robotic implementation for endoscopic endonasal or skull base neurosurgery remain limited. Therefore, the authors performed a systematic review of the literature to identify all articles that used robotic systems to access the sella or anterior, middle, or posterior cranial fossae. METHODS A systematic review of MEDLINE and PubMed in accordance with PRISMA guidelines performed for articles published between January 1, 1990, and August 1, 2021, was conducted to identify all robotic systems (autonomous, semiautonomous, or surgeon-controlled) used for skull base neurosurgical procedures. Cadaveric and human clinical studies were included. Studies with exclusively otorhinolaryngological applications or using robotic microscopes were excluded. RESULTS A total of 561 studies were identified from the initial search, of which 22 were included following full-text review. Transoral robotic surgery (TORS) using the da Vinci Surgical System was the most widely reported system (4 studies) utilized for skull base and pituitary fossa procedures; additionally, it has been reported for resection of sellar masses in 4 patients. Seven cadaveric studies used the da Vinci Surgical System to access the skull base using alternative, non–TORS approaches (e.g., transnasal, transmaxillary, and supraorbital). Five cadaveric studies investigated alternative systems to access the skull base. Six studies investigated the use of robotic endoscope holders. Advantages to robotic applications in skull base neurosurgery included improved lighting and 3D visualization, replication of more traditional gesture-based movements, and the ability for dexterous movements ordinarily constrained by small operative corridors. Limitations included the size and angulation capacity of the robot, lack of drilling components preventing fully robotic procedures, and cost. Robotic endoscope holders may have been particularly advantageous when the use of a surgical assistant or second surgeon was limited. CONCLUSIONS Robotic skull base neurosurgery has been growing in popularity and feasibility, but significant limitations remain. While robotic systems seem to have allowed for greater maneuverability and 3D visualization, their size and lack of neurosurgery-specific tools have continued to prevent widespread adoption into current practice. The next generation of robotic technologies should prioritize overcoming these limitations.

show abstract

Section: Future Directionsmentioning

confidence: 99%

Robotic and robot-assisted skull base neurosurgery: systematic review of current applications and future directions

Pangal

Coté

Ruzevick

et al. 2022

Neurosurgical Focus

View full text Add to dashboard Cite

show abstract

“…vascular injury) but the model has been trained on. 38 SOCALNet has room for improvement. For adverse events, the 1) accurate estimation of high-volume blood-loss and 2) detection of task failures may be prioritized as exsanguination is life-threatening.…”

Section: Socalnet Performance Compared To Expertsmentioning

confidence: 99%

Expert Surgeons and Deep Learning Models Can Predict the Outcome of Surgical Hemorrhage from One Minute of Video

Pangal

Kugener

Zhu

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Major vascular injury resulting in uncontrolled bleeding is a catastrophic and often fatal complication of minimally invasive surgery. At the outset of these events, surgeons do not know how much blood will be lost or whether they will successfully control the hemorrhage (achieve hemostasis). We evaluate the ability of a deep learning neural network (DNN) to predict hemostasis control ability using the first minute of surgical video and compare model performance with human experts viewing the same video. Methods: The publicly available SOCAL dataset contains 147 videos of attending and resident surgeons managing hemorrhage in a validated, high-fidelity cadaveric simulator. Videos are labeled with outcome and blood loss (mL). The first minute of 20 videos was shown to four, blinded, fellowship trained skull-base neurosurgery instructors, and to SOCALNet (a DNN trained on SOCAL videos). SOCALNet architecture included a convolutional network (ResNet) identifying spatial features and a recurrent network identifying temporal features (LSTM). Experts independently assessed surgeon skill, predicted outcome and blood loss (mL). Outcome and blood loss predictions were compared with SOCALNet. Results: Expert inter-rater reliability was 0.95. Experts correctly predicted 14/20 trials (Sensitivity: 82%, Specificity: 55%, Positive Predictive Value (PPV): 69%, Negative Predictive Value (NPV): 71%). SOCALNet correctly predicted 17/20 trials (Sensitivity 100%, Specificity 66%, PPV 79%, NPV 100%) and correctly identified all successful attempts. Expert predictions of the highest and lowest skill surgeons and expert predictions reported with maximum confidence were more accurate. Experts systematically underestimated blood loss (mean error -131 mL, RMSE 350 mL, R2 0.70) and fewer than half of expert predictions identified blood loss > 500mL (47.5%, 19/40). SOCALNet had superior performance (mean error -57 mL, RMSE 295mL, R2 0.74) and detected most episodes of blood loss > 500mL (80%, 8/10). In validation experiments, SOCALNet evaluation of a critical on-screen surgical maneuver and high/low-skill composite videos were concordant with expert evaluation. Conclusion: Using only the first minute of video, experts and SOCALNet can predict outcome and blood loss during surgical hemorrhage. Experts systematically underestimated blood loss, and SOCALNet had no false negatives. DNNs can provide accurate, meaningful assessments of surgical video. We call for the creation of datasets of surgical adverse events for quality improvement research.

show abstract

“…Wearable health devices will enable surgeons to remotely monitor patients, for an uninterrupted post-operative monitoring. These devices provide real-time data which can be analyzed by machine learning algorithms to provide remote feedback to surgeons, regarding the patients' condition [50] . To top it off, this occurs with minimal disruption to patients' daily lives.…”

Section: Ai In Enhancing Patient Carementioning

confidence: 99%

Pragmatism in transforming surgical workplace with artificial intelligence

Tamalvanan¹

2022

AIS

View full text Add to dashboard Cite

Computer vision in surgery

Cited by 102 publications

References 30 publications

Robotic and robot-assisted skull base neurosurgery: systematic review of current applications and future directions

Robotic and robot-assisted skull base neurosurgery: systematic review of current applications and future directions

Expert Surgeons and Deep Learning Models Can Predict the Outcome of Surgical Hemorrhage from One Minute of Video

Pragmatism in transforming surgical workplace with artificial intelligence

Contact Info

Product

Resources

About