Attitudes of Patients and Their Relatives Towards Artificial Intelligence in Neurosurgery

BACKGROUND: Artificial Intelligence (AI) may favorably support surgeons but may result in concern among patients and their relatives. OBJECTIVE: To evaluate attitudes of patients and their relatives towards the use of AI in neurosurgery. METHODS: In this two-stage cross-sectional survey, a qualitative survey was administered to a focus group of former patients to investigate their perception of AI and its role in neurosurgery. Five themes were identified and used to generate a case-based quantitative survey administered to inpatients and their relatives over a two-week period. Presented AI platforms were rated appropriate and acceptable using 5-point Likert scales. Demographic data was collected. A Chi Square test was performed to determine whether demographics influenced participants' attitudes. RESULTS: In the first stage, 20 participants responded. Five themes were identified: interpretation of imaging (4/20; 20%), operative planning (5/20; 25%), real-time alert of potential complications (10/20; 50%), partially autonomous surgery (6/20; 30%), fully autonomous surgery (3/20; 15%). In the second stage, 107 participants responded. The majority felt appropriate and acceptable to use AI for imaging interpretation (76.7%; 66.3%), operative planning (76.7%; 75.8%), real-time alert of potential complications (82.2%; 72.9%), and partially autonomous surgery (58%; 47.7%). Conversely, most did not feel that fully autonomous surgery was appropriate (27.1%) or acceptable (17.7%). Demographics did not have a significant influence on perception. CONCLUSIONS: The majority of patients and their relatives believed that AI has a role in neurosurgery and found it acceptable. Notable exceptions remain fully autonomous systems, with most wanting the neurosurgeon ultimately to remain in control

Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0)

OBJECTIVE: Surgical workflow analysis involves systematically breaking down operations into key phases and steps. Automatic analysis of this workflow has potential uses for surgical training, preoperative planning, and outcome prediction. Recent advances in machine learning (ML) and computer vision have allowed accurate automated workflow analysis of operative videos. In this Idea, Development, Exploration, Assessment, Long-term study (IDEAL) stage 0 study, the authors sought to use Touch Surgery for the development and validation of an ML-powered analysis of phases and steps in the endoscopic transsphenoidal approach (eTSA) for pituitary adenoma resection, a first for neurosurgery. METHODS: The surgical phases and steps of 50 anonymized eTSA operative videos were labeled by expert surgeons. Forty videos were used to train a combined convolutional and recurrent neural network model by Touch Surgery. Ten videos were used for model evaluation (accuracy, F1 score), comparing the phase and step recognition of surgeons to the automatic detection of the ML model. RESULTS: The longest phase was the sellar phase (median 28 minutes), followed by the nasal phase (median 22 minutes) and the closure phase (median 14 minutes). The longest steps were step 5 (tumor identification and excision, median 17 minutes); step 3 (posterior septectomy and removal of sphenoid septations, median 14 minutes); and step 4 (anterior sellar wall removal, median 10 minutes). There were substantial variations within the recorded procedures in terms of video appearances, step duration, and step order, with only 50% of videos containing all 7 steps performed sequentially in numerical order. Despite this, the model was able to output accurate recognition of surgical phases (91% accuracy, 90% F1 score) and steps (76% accuracy, 75% F1 score). CONCLUSIONS: In this IDEAL stage 0 study, ML techniques have been developed to automatically analyze operative videos of eTSA pituitary surgery. This technology has previously been shown to be acceptable to neurosurgical teams and patients. ML-based surgical workflow analysis has numerous potential uses-such as education (e.g., automatic indexing of contemporary operative videos for teaching), improved operative efficiency (e.g., orchestrating the entire surgical team to a common workflow), and improved patient outcomes (e.g., comparison of surgical techniques or early detection of adverse events). Future directions include the real-time integration of Touch Surgery into the live operative environment as an IDEAL stage 1 (first-in-human) study, and further development of underpinning ML models using larger data sets

Autonomous surgical robotic systems and the liability dilemma

Background: Advances in machine learning and robotics have allowed the development of increasingly autonomous robotic systems which are able to make decisions and learn from experience. This distribution of decisionmaking away from human supervision poses a legal challenge for determining liability. Methods: The iRobotSurgeon survey aimed to explore public opinion towards the issue of liability with robotic surgical systems. The survey included five hypothetical scenarios where a patient comes to harm and the respondent needs to determine who they believe is most responsible: the surgeon, the robot manufacturer, the hospital, or another party. Results: A total of 2,191 completed surveys were gathered evaluating 10,955 individual scenario responses from 78 countries spanning 6 continents. The survey demonstrated a pattern in which participants were sensitive to shifts from fully surgeon-controlled scenarios to scenarios in which robotic systems played a larger role in decision-making such that surgeons were blamed less. However, there was a limit to this shift with human surgeons still being ascribed blame in scenarios of autonomous robotic systems where humans had no role in decision-making. Importantly, there was no clear consensus among respondents where to allocate blame in the case of harm occurring from a fully autonomous system. Conclusions: The iRobotSurgeon Survey demonstrated a dilemma among respondents on who to blame when harm is caused by a fully autonomous surgical robotic system. Importantly, it also showed that the surgeon is ascribed blame even when they have had no role in decision-making which adds weight to concerns that human operators could act as “moral crumple zones” and bear the brunt of legal responsibility when a complex autonomous system causes harm

Genetic determinants of risk in pulmonary arterial hypertension: international genome-wide association studies and meta-analysis

Background Rare genetic variants cause pulmonary arterial hypertension, but the contribution of common genetic variation to disease risk and natural history is poorly characterised. We tested for genome-wide association for pulmonary arterial hypertension in large international cohorts and assessed the contribution of associated regions to outcomes. Methods We did two separate genome-wide association studies (GWAS) and a meta-analysis of pulmonary arterial hypertension. These GWAS used data from four international case-control studies across 11744 individuals with European ancestry (including 2085 patients). One GWAS used genotypes from 5895 whole-genome sequences and the other GWAS used genotyping array data from an additional 5849 individuals. Cross-validation of loci reaching genome-wide significance was sought by meta-analysis. Conditional analysis corrected for the most significant variants at each locus was used to resolve signals for multiple associations. We functionally annotated associated variants and tested associations with duration of survival. All-cause mortality was the primary endpoint in survival analyses. Findings A locus near SOX17 (rs10103692, odds ratio 1·80 [95% CI 1·55–2·08], p=5·13×10– ¹⁵) and a second locus in HLA-DPA1 and HLA-DPB1 (collectively referred to as HLA-DPA1/DPB1 here; rs2856830, 1·56 [1·42–1·71], p=7·65×10– ²⁰) within the class II MHC region were associated with pulmonary arterial hypertension. The SOX17 locus had two independent signals associated with pulmonary arterial hypertension (rs13266183, 1·36 [1·25–1·48], p=1·69×10– ¹²; and rs10103692). Functional and epigenomic data indicate that the risk variants near SOX17 alter gene regulation via an enhancer active in endothelial cells. Pulmonary arterial hypertension risk variants determined haplotype-specific enhancer activity, and CRISPR-mediated inhibition of the enhancer reduced SOX17 expression. The HLA-DPA1/DPB1 rs2856830 genotype was strongly associated with survival. Median survival from diagnosis in patients with pulmonary arterial hypertension with the C/C homozygous genotype was double (13·50 years [95% CI 12·07 to >13·50]) that of those with the T/T genotype (6·97 years [6·02–8·05]), despite similar baseline disease severity. Interpretation This is the first study to report that common genetic variation at loci in an enhancer near SOX17 and in HLA-DPA1/DPB1 is associated with pulmonary arterial hypertension. Impairment of SOX17 function might be more common in pulmonary arterial hypertension than suggested by rare mutations in SOX17. Further studies are needed to confirm the association between HLA typing or rs2856830 genotyping and survival, and to determine whether HLA typing or rs2856830 genotyping improves risk stratification in clinical practice or trials. Funding UK NIHR, BHF, UK MRC, Dinosaur Trust, NIH/NHLBI, ERS, EMBO, Wellcome Trust, EU, AHA, ACClinPharm, Netherlands CVRI, Dutch Heart Foundation, Dutch Federation of UMC, Netherlands OHRD and RNAS, German DFG, German BMBF, APH Paris, INSERM, Université Paris-Sud, and French ANR

Attitudes of patients and their relatives toward artificial intelligence in neurosurgery.

BACKGROUND: Artificial intelligence (AI) may favorably support surgeons but can result in concern among patients and their relatives. The aim of this study was to evaluate attitudes of patients and their relatives regarding use of AI in neurosurgery. METHODS: In a 2-stage cross-sectional survey, a qualitative survey was administered to a focus group of former patients to investigate their perception of AI and its role in neurosurgery. Five themes were identified and used to generate a case-based quantitative survey administered to inpatients and their relatives over a 2-week period. Presented AI platforms were rated appropriate and acceptable using 5-point Likert scales. Demographic data were collected. χ2 test was used to determine whether demographics influenced participants' attitudes. RESULTS: In the first stage, 20 participants responded. Five themes were identified: interpretation of imaging (4/20; 20%), operative planning (5/20; 25%), real-time alert of potential complications (10/20; 50%), partially autonomous surgery (6/20; 30%), and fully autonomous surgery (3/20; 15%). In the second stage, 107 participants responded. Most thought it appropriate and acceptable to use AI for imaging interpretation (76.7%; 66.3%), operative planning (76.7%; 75.8%), real-time alert of potential complications (82.2%; 72.9%), and partially autonomous surgery (58%; 47.7%). Conversely, most did not think that fully autonomous surgery was appropriate (27.1%) or acceptable (17.7%). Demographics did not have a significant influence on perception. CONCLUSIONS: Most patients and their relatives believed that AI has a role in neurosurgery and found it acceptable. Notable exceptions were fully autonomous systems, with most wanting the neurosurgeon ultimately to remain in control

Attitudes of the surgical team toward artificial intelligence in neurosurgery: an international two-stage cross-sectional survey.

BACKGROUND: Artificial Intelligence (AI) has the potential to disrupt how we diagnose and treat patients. Previous work by our group has demonstrated that the majority of patients and their relatives feel comfortable with the application of AI to augment surgical care. The aim of this study was to similarly evaluate the attitudes of surgeons and the wider surgical team towards the role of AI in neurosurgery. METHODS: In a two-stage cross sectional survey, an initial open-question qualitative survey was created to determine the perspective of the surgical team on AI in neurosurgery, including surgeons, anaesthetists, nurses, and theatre practitioners. Thematic analysis was performed to develop a second stage quantitative survey that was distributed via social media. We assessed the extent to which they agreed and were comfortable with real-world AI implementation using a 5-point Likert scale. RESULTS: In the first stage survey, 33 participants responded. Six main themes were identified: imaging interpretation and pre-operative diagnosis; co-ordination of the surgical team; operative planning; real-time alert of hazards and complications; autonomous surgery; post-operative management and follow-up. In the second stage, 100 participants responded. Responders somewhat agreed or strongly agreed about AI utilised for imaging interpretation (62%), operative planning (82%), co-ordination of the surgical team (70%), real-time alert of hazards and complications (85%), and autonomous surgery (66%). The role of AI within post-operative management and follow-up was less agreeable (49%). CONCLUSION: This survey highlights that the majority of surgeons and the wider surgical team both agree and are comfortable with the application of AI within neurosurgery

Supplementary Motor Area Syndrome After Brain Tumor Surgery: A Systematic Review

BACKGROUND: Supplementary motor area syndrome (SMAS) may occur after frontal tumor surgery, with variable presentation and outcomes. We reviewed the literature on postoperative SMAS after brain tumor resection.METHODS: PubMed, Web of Science, Scopus, and Cochrane were searched following the PRISMA guidelines to include studies reporting SMAS after brain tumor resection.RESULTS: We included 31 studies encompassing 236 patients. Most tumors were gliomas (94.5%), frequently of low grade (61.4%). Most lesions were located on the left hemisphere (64.4%), involving the supplementary motor area (61.4%) and the cingulate gyrus (20.8%). Tractography and functional magnetic resonance imaging evaluation were completed in 45 (19.1%) and 26 (11%) patients. Gross total resection was achieved in 46.3% patients and complete SMA resection in 69.4%. A total of 215 procedures (91.1%) used intraoperative neuromonitoring mostly consisting of direct cortical/subcortical stimulation (56.4%), motor (33.9%), and somatosensory (25.4%) evoked potentials. Postoperative SMAS symptoms occurred within 24 hours after surgery, characterized by motor deficits (97%), including paresis (68.6%) and hemiplegia (16.1%), and speech disorders (53%), including hesitancy (24.2%) and mutism (22%). Average SMAS duration was 45 days (range, 1-365 days), with total resolution occurring in 188 patients (79.7%) and partial improvement in 46 (19.5%). Forty-eight patients (20.3%) had persisting symptoms, mostly speech hesitancy (60.4%) and fine motor disorders (45.8%).CONCLUSIONS: Postoperative SMAS may occur within the first 24 hours after mesial frontal tumor surgery. Preoperative mapping and intraoperative neuromonitoring may assist resection and predict outcomes. Neuroplasticity and interhemispheric connectivity play a major role in resolution

Thoracic spinal extradural arachnoid cyst: A case report and literature review

Background: Spinal extradural arachnoid cysts (SEDACs) are rare and are variously attributed to congenital, traumatic, or inflammatory etiologies. Here, we report a 70-year-old male who presented with a T11-T12 SEDAC and an incidental craniovertebral junction (CVJ) meningioma. Case Description: A 70-year-old male presented with progressive bilateral lower limb weakness and paresthesias. The thoracic MRI identified an extradural arachnoid cystic lesion at the T11-T12 level. In addition, the brain/ cervical MR documented an incidental meningioma at the CVJ. The patient underwent T11-T12 laminectomy for fenestration/removal of the extradural arachnoid cyst resulting in immediate cord decompression and neurological recovery. The histologic examination was consistent with a SEDAC who underwent successful resection of the SEDAC that resulted in symptom resolution. Conclusion: We presented a 71-year-old male with a thoracic SEDAC and an incidental CVJ meningioma, where resection of the SEDAC resulted in symptom resolution

Spinal epidural abscess due to acute pyelonephritis

Background: Spinal epidural abscesses are rare and are misdiagnosed in up to 75% of cases. Fever, back pain, and neurological deficits are part of the classical triad. Here, the authors report a patient with a L2-L5 spinal epidural abscess with the left paravertebral extension attributed to acute pyelonephritis. Case Description: A 54-year-old female presented with persistent low back pain and lower extremity weakness accompanied by paresthesias. Previously, she had been hospitalized with the left acute pyelonephritis. The lumbosacral MRI documented a T12/L5 anterior epidural abscess with ring enhancement on the contrast study; the maximum diameter of the abscess at the L2-L3 level contributed to severe cauda equina compression. She underwent a L2/L4 decompressive laminectomy with drainage of the intraspinal/extradural and paravertebral components. Intraoperative microbiological sampling grew Staphylococcus aureus for which she then received targeted antibiotic therapy. Fifteen days later, she was walking adequately when discharged. Conclusion: Thoracolumbar epidural abscesses are rare. They must be considered among the differential diagnoses when patients present with acute back pain, fever, and new neurological deficits following prior treatment for acute pyelonephritis