Surgical management of pediatric spinal aneurysmal bone cysts: patient series.

BACKGROUND: Aneurysmal bone cysts (ABCs) are rare, highly vascular osteolytic bone lesions that predominantly affect pediatric populations. This report evaluates the clinicopathological data of pediatric patients with spinal ABCs. The medical records for all patients at Children's Hospital Los Angeles with biopsy-proven ABCs of the spine between 1998 and 2018 were evaluated. OBSERVATIONS: Seventeen patients, 6 males and 11 females, were identified. The mean age at surgery was 10.4 years (range, 3.5-20 years). The most common presenting complaint was pain at the lesion site 16/17 (94%), followed by lower-extremity weakness 8/17 (47%). Resection and intralesional curettage were performed in all patients. Three (18%) of 17 patients underwent selective arterial embolization prior to resection. Spinal stability was compromised in 15 of 17 patients (88%), requiring instrumented fusion. Five (29%) of the 17 patients received additional therapy including radiation, calcitonin-methylprednisolone, or phenol. Four (23.5%) of 17 patients experienced a recurrence, and the mean time to recurrence was 15 months. The postoperative follow-up ranged from 6 to 108 months (median, 28 months). Reoperation occurred after an average of 35 months. At the recent follow-up, patients were free of disease. LESSONS: Gross-total resection by intralesional curettage with case-dependent instrumented spinal fusion for instability remains an effective strategy for managing pediatric spinal ABCs. Long-term follow-up is necessary to detect tumor recurrence.

Machine Learning Analysis of Physical Activity Data to Classify Postural Dysfunction.

BACKGROUND: Machine learning (ML) analysis of biometric data in non-controlled environments is underexplored. OBJECTIVE: To evaluate whether ML analysis of physical activity data can be employed to classify whether individuals have postural dysfunction in middle-aged and older individuals. METHODS: A 1 week period of physical activity was measured by a waist-worn uni-axial accelerometer during the 2003-2004 National Health and Nutrition Examination Survey sampling period. Features of physical activity along with basic demographic information (42 variables) were paired with ML models to predict the success or failure of a standard 30 s modified Romberg test during which participants had their eyes closed and stood upon a 3-inch compliant surface. Model performance was evaluated by area under the receiver operating characteristic curve (AUC-ROC), balanced accuracy, and F1-score. RESULTS: The cohort was comprised of 1625 participants ≥40 years (median age 61, IQR 51-71). Approximately half (47%) were diagnosed with postural dysfunction having failed the binarized (pass/fail) scoring mechanism of the modified Romberg exam. Five ML models were trained on the classification task, achieving AUC values ranging from 0.67 to 0.73. The support vector machine (SVM) and a gradient-boosted model, XGBoost, achieved the highest AUC of 0.73 (SD 0.71-0.75). Age was the most important variable for SVM classification, followed by four features that evaluated accelerometer counts at various thresholds, including those delineating total, moderate, and moderate-vigorous activity. CONCLUSIONS: ML analysis of accelerometer-derived physical activity data to classify postural dysfunction in middle-aged and older individuals is feasible in real-world environments such as the home. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:3529-3533, 2023.

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review.

OBJECTIVE: This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC). DATA SOURCES: Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE. REVIEW METHODS: A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness. CONCLUSIONS: AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality. IMPLICATIONS FOR PRACTICE: AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.

Virtual Versus In-Person Head and Neck Physical Examination Training in Medical Students: A Pilot Assessment.

OBJECTIVE: To compare the effect of virtual and in-person head and neck physical examination training events on medical student confidence in performing examination maneuvers and seeking mentorship from otolaryngology faculty and residents. METHODS: Training events were held with first-year medical student volunteers in 2020 (in-person) and 2021 (virtual). Participants in both cohorts were given didactics on head and neck cancer, trained to perform a head and neck physical examination, and demonstrated their clinical skills to otolaryngology faculty and residents. Pre- and post-training surveys were utilized to assess the following outcomes: participant head and neck cancer knowledge, confidence in performing examination maneuvers, and confidence in seeking mentorship in otolaryngology. Differences in outcomes between training settings were assessed by comparing participant survey responses pre- and post- training. RESULTS: Both in-person and virtual training modalities improved participant confidence in performing the physical examination. There was no significant difference in the degree of improvement between training types. In-person training significantly increased participant confidence in seeking mentorship from otolaryngology faculty and residents (P = .003), while virtual training did not (P = .194). CONCLUSION: Virtual training modalities are feasible methods of teaching the head and neck physical examination. Instruction through a video conferencing platform has the potential to be incorporated into traditional in-person medical education in a permanent fashion. This pilot study can inform future studies directly comparing in-person and virtual physical examination training modalities.

Development of an ultrafast brain MR neuronavigation protocol for ventricular shunt placement.

OBJECTIVE: Advancements in MRI technology have provided improved ways to acquire imaging data and to more seamlessly incorporate MRI into modern pediatric surgical practice. One such situation is image-guided navigation for pediatric neurosurgical procedures, including intracranial catheter placement. Image-guided surgery (IGS) requires acquisition of CT or MR images, but the former carries the risk of ionizing radiation and the latter is associated with long scan times and often requires pediatric patients to be sedated. The objective of this project was to circumvent the use of CT and standard-sequence MRI in ventricular neuronavigation by investigating the use of fast MR sequences on the basis of 3 criteria: scan duration comparable to that of CT acquisition, visualization of ventricular morphology, and image registration with surface renderings comparable to standard of care. The aim of this work was to report image development, implementation, and results of registration accuracy testing in healthy subjects. METHODS: The authors formulated 11 candidate MR sequences on the basis of the standard IGS protocol, and various scan parameters were modified, such as k-space readout direction, partial k-space acquisition, sparse sampling of k-space (i.e., compressed sensing), in-plane spatial resolution, and slice thickness. To evaluate registration accuracy, the authors calculated target registration error (TRE). A candidate sequence was selected for further evaluation in 10 healthy subjects. RESULTS: The authors identified a candidate imaging protocol, termed presurgical imaging with compressed sensing for time optimization (PICO). Acquisition of the PICO protocol takes 25 seconds. The authors demonstrated noninferior TRE for PICO (3.00 ± 0.19 mm) in comparison with the default MRI neuronavigation protocol (3.35 ± 0.20 mm, p = 0.20). CONCLUSIONS: The developed and tested sequence of this work allowed accurate intraoperative image registration and provided sufficient parenchymal contrast for visualization of ventricular anatomy. Further investigations will evaluate use of the PICO protocol as a substitute for CT and conventional MRI protocols in ventricular neuronavigation.

Intractable Bleeding Following an Awake Bedside Injection Laryngoplasty in a Patient on KVAD ECMO.

INTRODUCTION: Awake injection laryngoplasty (IL) is becoming increasingly utilized in the inpatient setting, especially as a therapeutic option for patients with vocal fold immobility immediately following cardiothoracic surgery. While prior studies consistently demonstrate complication rates below 3%, significant bleeding has not been reported as a major complication in any awake IL case series. The objective of this report is to highlight a case of intractable bleeding following awake inpatient bedside IL in a patient on KVAD (Koji Takeda Ventricular Assist Device) extracorporeal membrane oxygenation (ECMO). METHODS: Case Report. RESULTS: A 24-year-old female admitted to the cardiac ICU for asystole was placed on KVAD ECMO for heart failure of unknown etiology. She was extubated and listed for cardiac transplant. On postoperative day 14, she underwent a left vocal fold injection at bedside to treat fold paralysis with a large glottic gap causing her complete aphonia, dysphagia, and chronic aspiration. Seven hours post procedure, the patient had to be reintubated due to intractable bleeding. A direct laryngoscopy was performed at bedside and continuous trickle of blood from the injection site at the superior posterior lateral surface of the vocal fold was stopped using a combination of epi-pledgets and hemostatic matrix. The same procedure had to be performed again due to further bleeding 2 days later and permanent hemostasis was achieved. During the week post injection, the patient required transfusion of 5 units of pRBC's. One month later the patient underwent successful orthotopic heart transplantation and was transferred from the ICU to a stepdown unit, and then a rehabilitation unit. No further IL hemorrhage occurred. CONCLUSION: Although a few studies have discussed the safety of IL in patients receiving anticoagulation, this case report demonstrates intractable bleeding requiring intubation and intervention to achieve hemostasis in a patient on KVAD ECMO. This report highlights the importance of weighing the risks and benefits of vocal fold injection in this patient population.

Survival Analysis Using Surgeon Skill Metrics and Patient Factors to Predict Urinary Continence Recovery After Robot-assisted Radical Prostatectomy.

BACKGROUND: It has been shown that metrics recorded for instrument kinematics during robotic surgery can predict urinary continence outcomes. OBJECTIVE: To evaluate the contributions of patient and treatment factors, surgeon efficiency metrics, and surgeon technical skill scores, especially for vesicourethral anastomosis (VUA), to models predicting urinary continence recovery following robot-assisted radical prostatectomy (RARP). DESIGN, SETTING, AND PARTICIPANTS: Automated performance metrics (APMs; instrument kinematics and system events) and patient data were collected for RARPs performed from July 2016 to December 2017. Robotic Anastomosis Competency Evaluation (RACE) scores during VUA were manually evaluated. Training datasets included: (1) patient factors; (2) summarized APMs (reported over RARP steps); (3) detailed APMs (reported over suturing phases of VUA); and (4) technical skills (RACE). Feature selection was used to compress the dimensionality of the inputs. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The study outcome was urinary continence recovery, defined as use of 0 or 1 safety pads per day. Two predictive models (Cox proportional hazards [CoxPH] and deep learning survival analysis [DeepSurv]) were used. RESULTS AND LIMITATIONS: Of 115 patients undergoing RARP, 89 (77.4%) recovered their urinary continence and the median recovery time was 166 d (interquartile range [IQR] 82-337). VUAs were performed by 23 surgeons. The median RACE score was 28/30 (IQR 27-29). Among the individual datasets, technical skills (RACE) produced the best models (C index: CoxPH 0.695, DeepSurv: 0.708). Among summary APMs, posterior/anterior VUA yielded superior model performance over other RARP steps (C index 0.543-0.592). Among detailed APMs, metrics for needle driving yielded top-performing models (C index 0.614-0.655) over other suturing phases. DeepSurv models consistently outperformed CoxPH; both approaches performed best when provided with all the datasets. Limitations include feature selection, which may have excluded relevant information but prevented overfitting. CONCLUSIONS: Technical skills and "needle driving" APMs during VUA were most contributory. The best-performing model used synergistic data from all datasets. PATIENT SUMMARY: One of the steps in robot-assisted surgical removal of the prostate involves joining the bladder to the urethra. Detailed information on surgeon performance for this step improved the accuracy of predicting recovery of urinary continence among men undergoing this operation for prostate cancer.

Innovations in Urologic Surgical Training.

PURPOSE OF REVIEW: This review aims to summarize innovations in urologic surgical training in the past 5 years. RECENT FINDINGS: Many assessment tools have been developed to objectively evaluate surgical skills and provide structured feedback to urologic trainees. A variety of simulation modalities (i.e., virtual/augmented reality, dry-lab, animal, and cadaver) have been utilized to facilitate the acquisition of surgical skills outside the high-stakes operating room environment. Three-dimensional printing has been used to create high-fidelity, immersive dry-lab models at a reasonable cost. Non-technical skills such as teamwork and decision-making have gained more attention. Structured surgical video review has been shown to improve surgical skills not only for trainees but also for qualified surgeons. Research and development in urologic surgical training has been active in the past 5 years. Despite these advances, there is still an unfulfilled need for a standardized surgical training program covering both technical and non-technical skills.

ERAS for Head and Neck Tissue Transfer Reduces Opioid Usage, Peak Pain Scores, and Blood Utilization.

OBJECTIVES: We implement a novel enhanced recovery after surgery (ERAS) protocol with pre-operative non-opioid loading, total intravenous anesthesia, multimodal peri-operative analgesia, and restricted red blood cell (pRBC) transfusions. 1) Compare differences in mean postoperative peak pain scores, opioid usage, and pRBC transfusions. 2) Examine changes in overall length of stay (LOS), intensive care unit LOS, complications, and 30-day readmissions. METHODS: Retrospective cohort study comparing 132 ERAS vs. 66 non-ERAS patients after HNC tissue transfer reconstruction. Data was collected in a double-blind fashion by two teams. RESULTS: Mean postoperative peak pain scores were lower in the ERAS group up to postoperative day (POD) 2. POD0: 4.6 ± 3.6 vs. 6.5 ± 3.5; P = .004) (POD1: 5.2 ± 3.5 vs. 7.3 ± 2.3; P = .002) (POD2: 4.1 ± 3.5 vs. 6.6 ± 2.8; P = .000). Opioid utilization, converted into morphine milligram equivalents, was decreased in the ERAS group (POD0: 6.0 ± 9.8 vs. 10.3 ± 10.8; P = .010) (POD1: 14.1 ± 22.1 vs. 34.2 ± 23.2; P = .000) (POD2: 11.4 ± 19.7 vs. 37.6 ± 31.7; P = .000) (POD3: 13.7 ± 20.5 vs. 37.9 ± 42.3; P = .000) (POD4: 11.7 ± 17.9 vs. 36.2 ± 39.2; P = .000) (POD5: 10.3 ± 17.9 vs. 35.4 ± 45.6; P = .000). Mean pRBC transfusion rate was lower in ERAS patients (2.1 vs. 3.1 units, P = .017). There were no differences between ERAS and non-ERAS patients in hospital LOS, ICU LOS, complication rates, and 30-day readmissions. CONCLUSION: Our ERAS pathway reduced postoperative pain, opioid usage, and pRBC transfusions after HNC reconstruction. These benefits were obtained without an increase in hospital or ICU LOS, complications, or readmission rates. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:E792-E799, 2021.

A Novel Dissection Gesture Classification to Characterize Robotic Dissection Technique for Renal Hilar Dissection.

PURPOSE: Deconstruction of robotic surgical gestures into semantic vocabulary yields an effective tool for surgical education. In this study we disassembled tissue dissection into basic gestures, created a classification system, and showed its ability to distinguish between experts and novices. MATERIALS AND METHODS: Videos of renal hilum preparation during robotic assisted partial nephrectomies were manually reviewed to identify all discrete surgical movements. Identified dissection movements were classified into distinct gestures based on the consensus of 6 expert surgeons. This classification system was then employed to compare expert and novice dissection patterns during the renal hilum preparation. RESULTS: A total of 40 robotic renal hilum preparation videos were reviewed, representing 16 from 6 expert surgeons (100 or more robotic cases) and 24 from 13 novice surgeons (fewer than 100 robotic cases). Overall 9,819 surgical movements were identified, including 5,667 dissection movements and 4,152 supporting movements. Nine distinct dissection gestures were identified and classified into the 3 categories of single blunt dissection (spread, peel/push, hook), single sharp dissection (cold cut, hot cut and burn dissect) and combination gestures (pedicalize, 2-hand spread, and coagulate then cut). Experts completed 5 of 9 dissection gestures more efficiently than novices (p ≤0.033). In consideration of specific anatomical locations, experts used more peel/push and less hot cut while dissecting the renal vein (p <0.001), and used more pedicalize while dissecting the renal artery (p <0.001). CONCLUSIONS: Using this novel dissection gesture classification system, key differences in dissection patterns can be found between experts/novices. This comprehensive classification of dissection gestures may be broadly applied to streamline surgical education.

Machine learning in the optimization of robotics in the operative field.

PURPOSE OF REVIEW: The increasing use of robotics in urologic surgery facilitates collection of 'big data'. Machine learning enables computers to infer patterns from large datasets. This review aims to highlight recent findings and applications of machine learning in robotic-assisted urologic surgery. RECENT FINDINGS: Machine learning has been used in surgical performance assessment and skill training, surgical candidate selection, and autonomous surgery. Autonomous segmentation and classification of surgical data have been explored, which serves as the stepping-stone for providing real-time surgical assessment and ultimately, improve surgical safety and quality. Predictive machine learning models have been created to guide appropriate surgical candidate selection, whereas intraoperative machine learning algorithms have been designed to provide 3-D augmented reality and real-time surgical margin checks. Reinforcement-learning strategies have been utilized in autonomous robotic surgery, and the combination of expert demonstrations and trial-and-error learning by the robot itself is a promising approach towards autonomy. SUMMARY: Robot-assisted urologic surgery coupled with machine learning is a burgeoning area of study that demonstrates exciting potential. However, further validation and clinical trials are required to ensure the safety and efficacy of incorporating machine learning into surgical practice.