Endoscopic Video Research Articles

A convolutional neural network for bleeding detection in capsule endoscopy using real clinical data

Background The goal of the present study was to develop a convolutional neural network for the detection of bleedings in capsule endoscopy videos using realistic clinical data from one single-centre. Methods Capsule endoscopy videos from all 133 patients (79 male, 54 female; meanage = 53.73 years, SDage = 26.13) who underwent capsule endoscopy at our institution between January 2014 and August 2018 were screened for pathology. All videos were screened for pathology by two independent capsule experts and confirmed findings were checked again by a third capsule expert. From these videos, 125 pathological findings (individual episodes of bleeding spanning a total of 5696 images) and 103 non-pathological findings (sections of normal mucosal tissue without pathologies spanning a total of 7420 images) were used to develop and validate a neural network (Inception V3) using transfer learning. Results The overall accuracy of the model for the detection of bleedings was 90.6% [95%CI: 89.4%–91.7%], with a sensitivity of 89.4% [95%CI: 87.6%–91.2%] and a specificity of 91.7% [95%CI: 90.1%–93.2%]. Conclusion Our results show that neural networks can detect bleedings in capsule endoscopy videos under realistic, clinical conditions with an accuracy of 90.6%, potentially reducing reading time per capsule and helping to improve diagnostic accuracy.

Intraoperative laparoscopic photoacoustic image guidance system in the da Vinci surgical system.

This paper describes a framework allowing intraoperative photoacoustic (PA) imaging integrated into minimally invasive surgical systems. PA is an emerging imaging modality that combines the high penetration of ultrasound (US) imaging with high optical contrast. With PA imaging, a surgical robot can provide intraoperative neurovascular guidance to the operating physician, alerting them of the presence of vital substrate anatomy invisible to the naked eye, preventing complications such as hemorrhage and paralysis. Our proposed framework is designed to work with the da Vinci surgical system: real-time PA images produced by the framework are superimposed on the endoscopic video feed with an augmented reality overlay, thus enabling intuitive three-dimensional localization of critical anatomy. To evaluate the accuracy of the proposed framework, we first conducted experimental studies in a phantom with known geometry, which revealed a volumetric reconstruction error of 1.20 ± 0.71 mm. We also conducted an ex vivo study by embedding blood-filled tubes into chicken breast, demonstrating the successful real-time PA-augmented vessel visualization onto the endoscopic view. These results suggest that the proposed framework could provide anatomical and functional feedback to surgeons and it has the potential to be incorporated into robot-assisted minimally invasive surgical procedures.

Application and evaluation of surgical tool and tool tip recognition based on Convolutional Neural Network in multiple endoscopic surgical scenarios.

In recent years, computer-assisted intervention and robot-assisted surgery are receiving increasing attention. The need for real-time identification and tracking of surgical tools and tool tips is constantly demanding. A series of researches focusing on surgical tool tracking and identification have been performed. However, the size of dataset, the sensitivity/precision, and the response time of these studies were limited. In this work, we developed and utilized an automated method based on Convolutional Neural Network (CNN) and You Only Look Once (YOLO) v3 algorithm to locate and identify surgical tools and tool tips covering five different surgical scenarios. An algorithm of object detection was applied to identify and locate the surgical tools and tool tips. DarkNet-19 was used as Backbone Network and YOLOv3 was modified and applied for the detection. We included a series of 181 endoscopy videos covering 5 different surgical scenarios: pancreatic surgery, thyroid surgery, colon surgery, gastric surgery, and external scenes. A total amount of 25,333 images containing 94,463 targets were collected. Training and test sets were divided in a proportion of 2.5:1. The data sets were openly stored at the Kaggle database. Under an Intersection over Union threshold of 0.5, the overall sensitivity and precision rate of the model were 93.02% and 89.61% for tool recognition and 87.05% and 83.57% for tool tip recognition, respectively. The model demonstrated the highest tool and tool tip recognition sensitivity and precision rate under external scenes. Among the four different internal surgical scenes, the network had better performances in pancreatic and colon surgeries and poorer performances in gastric and thyroid surgeries. We developed a surgical tool and tool tip recognition model based on CNN and YOLOv3. Validation of our model demonstrated satisfactory precision, accuracy, and robustness across different surgical scenes.

Effects of Bit Chewing on Gastric Emptying, Small Intestinal Transit, and Orocecal Transit Times in Clinically Normal Horses.

Ileus is a common life-threatening problem in horses, and currently available treatments may be ineffective. The purpose of this study was to determine whether bit chewing, a form of sham feeding, decreases the gastric emptying time (GET), small intestinal transit time (SITT), and total orocecal transit time (OCTT) in clinically normal horses in a prospective crossover study. Nine healthy horses were acclimated and fed a standardized diet. Following 24 h of fasting, self-contained video endoscopy capsules and acetaminophen were administered into the stomach via a nasogastric tube. Each horse underwent experimental (bit chewing for 20 min every 6 h) or control (no bit chewing) conditions, with a 3-week minimum washout period between conditions. The horses were enrolled in either part of the study until all video capsules were retrieved and/or 30 days lapsed. The video capsules were recovered from manure, and GET, SITT, and OCTT were determined from a video analysis. Bit chewing significantly decreased OCTT (p = 0.015) compared to the control conditions. Bit chewing decreased GET and SITT, but the differences were not significant. The mean (median) times determined via the video capsule analysis for the bit-chewing conditions were as follows: GET, 2.34 h (2.86 h); SITT, 3.22 h (3.65 h); and OCTT, 5.13 h (6.15 h), and for the control conditions, they were as follows: GET, 3.93 h (5 h); SITT, 3.79 h (4.4 h); and OCTT, 8.02 h (9.92 h). Bit chewing decreased OCTT in healthy horses. Because this segment of the gastrointestinal tract is frequently affected by ileus, bit chewing may be a safe and inexpensive intervention for that condition in horses. Further investigation in clinical patients with ileus is warranted.

Preoperative prediction of sinonasal papilloma by artificial intelligence using nasal video endoscopy: a retrospective study

Sinonasal inverted papilloma (IP) is at risk of recurrence and malignancy, and early diagnosis using nasal endoscopy is essential. We thus developed a diagnostic system using artificial intelligence (AI) to identify nasal sinus papilloma. Endoscopic surgery videos of 53 patients undergoing endoscopic sinus surgery were edited to train and evaluate deep neural network models and then a diagnostic system was developed. The correct diagnosis rate based on visual examination by otolaryngologists was also evaluated using the same videos and compared with that of the AI diagnostic system patients. Main outcomes evaluated included the percentage of correct diagnoses compared to AI diagnosis and the correct diagnosis rate for otolaryngologists based on years of practice experience. The diagnostic system had an area under the curve of 0.874, accuracy of 0.843, false positive rate of 0.124, and false negative rate of 0.191. The average correct diagnosis rate among otolaryngologists was 69.4%, indicating that the AI was highly accurate. Evidently, although the number of cases was small, a highly accurate diagnostic system was created. Future studies with larger samples to improve the accuracy of the system and expand the range of diseases that can be detected for more clinical applications are warranted.

De-redundancy in wireless capsule endoscopy video sequences using correspondence matching and motion analysis

De-redundancy in wireless capsule endoscopy video sequences using correspondence matching and motion analysis

Deep learning for real-time detection of nasopharyngeal carcinoma during nasopharyngeal endoscopy

Nasopharyngeal carcinoma (NPC) is known for high curability during early stage of the disease, and early diagnosis relies on nasopharyngeal endoscopy and subsequent pathological biopsy. To enhance the early diagnosis rate by aiding physicians in the real-time identification of NPC and directing biopsy site selection during endoscopy, we assembled a dataset comprising 2,429 nasopharyngeal endoscopy video frames from 690 patients across three medical centers. With these data, we developed a deep learning-based NPC detection model using the you only look once (YOLO) network. Our model demonstrated high performance, with precision, recall, mean average precision, and F1-score values of 0.977, 0.943, 0.977, and 0.960, respectively, for internal test set and 0.825, 0.743, 0.814, and 0.780 for external test set at 0.5 intersection over union. Remarkably, our model demonstrated a high inference speed (52.9 FPS), surpassing the average frame rate (25.0 FPS) of endoscopy videos, thus making real-time detection in endoscopy feasible.

Classifying Vocal Folds Fixation from Endoscopic Videos with Machine Learning.

Vocal folds motility evaluation is paramount in both the assessment of functional deficits and in the accurate staging of neoplastic disease of the glottis. Diagnostic endoscopy, and in particular videoendoscopy, is nowadays the method through which the motility is estimated. The clinical diagnosis, however, relies on the examination of the videoendoscopic frames, which is a subjective and professional-dependent task. Hence, a more rigorous, objective, reliable, and repeatable method is needed. To support clinicians, this paper proposes a machine learning (ML) approach for vocal cords motility classification. From the endoscopic videos of 186 patients with both vocal cords preserved motility and fixation, a dataset of 558 images relative to the two classes was extracted. Successively, a number of features was retrieved from the images and used to train and test four well-grounded ML classifiers. From test results, the best performance was achieved using XGBoost, with precision = 0.82, recall = 0.82, F1 score = 0.82, and accuracy = 0.82. After comparing the most relevant ML models, we believe that this approach could provide precise and reliable support to clinical evaluation.Clinical Relevance- This research represents an important advancement in the state-of-the-art of computer-assisted otolaryngology, to develop an effective tool for motility assessment in the clinical practice.

Optical Flow Glottovibrogram for the examination of vocal fold pathology.

Laryngeal high-speed video endoscopy is performed to examine the cycles of vocal fold vibrations in detail and to diagnose voice abnormalities. One of the recent image processing techniques for visualizing vocal fold vibration is optical flow-based playbacks, which include optical flow kymograms (OFKG) for local dynamics, optical flow glottovibrogram (OFGVG) and glottal optical flow waveforms (GOFW) for global dynamics. In recent times, various optical flow computing algorithms have been developed. In this paper, we used four well-known optical flow algorithms Horn Schunk, Lucas Kanade, Gunnar Farneback, and TVL1 to construct the optical flow playbacks. The proposed playback reliability is examined by comparing them to traditional representations such as Phonovibrogram (PVG). Since PVG and OFGVG are interconnected, a comparison study was carried out to better comprehend their interaction.Clinical Relevance- Both OFGVG and PVG add to the precision of interpreting pathological conditions by offering complementary information to the conventional spatiotemporal representations.

Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video.

Artificial-intelligence-based computer-aided diagnosis (CAD) systems have developed remarkably in recent years. These systems can help increase the adenoma detection rate (ADR), an important quality indicator in colonoscopies. While there have been many still-image-based studies on the usefulness of CAD, few have reported on its usefulness using actual clinical videos. However, no studies have compared the CAD group and control groups using the exact same case videos. This study aimed to determine whether CAD or endoscopists were superior in identifying colorectal neoplastic lesions in videos. In this study, we examined 34 lesions from 21 cases. CAD performed better than four of the six endoscopists (three experts and three beginners), including all the beginners. The time to lesion detection with beginners and experts was 2.147 ± 1.118 s and 1.394 ± 0.805 s, respectively, with significant differences between beginners and experts (p < 0.001) and between beginners and CAD (both p < 0.001). The time to lesion detection was significantly shorter for experts and CAD than for beginners. No significant difference was found between experts and CAD (p = 1.000). CAD could be useful as a diagnostic support tool for beginners to bridge the experience gap with experts.

Consistencies among miscellaneous scales for evaluation of post-stroke dysphagia.

Post-stroke dysphagia (PSD) is the most common type of dysphagia. Stroke patients with sustained dysphagia have poorer outcomes. The severity of PSD is assessed using miscellaneous scales with unknown consistencies. We aim to investigate the consistencies among miscellaneous scales, which could aid in the assessment of PSD. A total of 49 PSD patients were enrolled. Functional Oral Intake Scale (FOIS), Dysphagia Severity Scale (DSS), Ohkuma Questionnaire, Eating Assessment Tool-10, and Repetitive Saliva Swallowing Test were performed. FOIS was performed by physicians, and DSS was conducted by both the physicians and nurses; the physicians used either videofluoroscopy (VF) or videoendoscopy (VE) for evaluation; while, the nurses assessed PSD by observation and subjective judgment. When using VF (VF-DSS and VF-FOIS) as the gold standard for the evaluation, VE-FOIS (κ = 0.625, 95% CI 0.300-0.950, p < 0.001) has a substantial agreement with VF-FOIS, and VE-DSS (κ = 0.381, 95% CI 0.127-0.636, p = 0.007) has a fair agreement with VF-DSS. The weighted kappa of FOIS to DSS in VE (weighted κ = 0.577, 95% CI 0.414-0.740, p < 0.001) is not lower than that in VF (weighted kappa = 0.249, 95% CI 0.136-0.362, p < 0.001). For both DSS and FOIS, only VE has a statistically significant agreement with VF. Though VF has been viewed as the traditional gold standard of dysphagia screening, it has the limitations of being invasive and equipment dependent. For PSD, VE could be considered as a substitution when VF is not available or suitable.

Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study.

Artificial intelligence (AI) uses deep learning functionalities that may enhance the detection of early gastric cancer during endoscopy. An AI-based endoscopic system for upper endoscopy was recently developed in Japan. We aim to validate this AI-based system in a Singaporean cohort. There were 300 de-identified still images prepared from endoscopy video files obtained from subjects that underwent gastroscopy in National University Hospital (NUH). Five specialists and 6 non-specialists (trainees) from NUH were assigned to read and categorize the images into "neoplastic" or "non-neoplastic." Results were then compared with the readings performed by the endoscopic AI system. The mean accuracy, sensitivity, and specificity for the 11 endoscopists were 0.847, 0.525, and 0.872, respectively. These values for the AI-based system were 0.777, 0.591, and 0.791, respectively. While AI in general did not perform better than endoscopists on the whole, in the subgroup of high-grade dysplastic lesions, only 29.1% were picked up by the endoscopist rating, but 80% were classified as neoplastic by AI (P=0.0011). The average diagnostic time was also faster in AI compared with endoscopists (677.1s vs 42.02s (P<0.001). We demonstrated that an AI system developed in another health system was comparable in diagnostic accuracy in the evaluation of static images. AI systems are faster and not fatigable and may have a role in augmenting human diagnosis during endoscopy. With more advances in AI and larger studies to support its efficacy it would likely play a larger role in screening endoscopy in future.

Deep Learning Architectures for Abnormality Detection in Endoscopy Videos

Deep Learning Architectures for Abnormality Detection in Endoscopy Videos

Process model analysis of parenchyma sparing laparoscopic liver surgery to recognize surgical steps and predict impact of new technologies

BackgroundSurgical process model (SPM) analysis is a great means to predict the surgical steps in a procedure as well as to predict the potential impact of new technologies. Especially in complicated and high-volume treatments, such as parenchyma sparing laparoscopic liver resection (LLR), profound process knowledge is essential for enabling improving surgical quality and efficiency.MethodsVideos of thirteen parenchyma sparing LLR were analyzed to extract the duration and sequence of surgical steps according to the process model. The videos were categorized into three groups, based on the tumor locations. Next, a detailed discrete events simulation model (DESM) of LLR was built, based on the process model and the process data obtained from the endoscopic videos. Furthermore, the impact of using a navigation platform on the total duration of the LLR was studied with the simulation model by assessing three different scenarios: (i) no navigation platform, (ii) conservative positive effect, and (iii) optimistic positive effect.ResultsThe possible variations of sequences of surgical steps in performing parenchyma sparing depending on the tumor locations were established. The statistically most probable chain of surgical steps was predicted, which could be used to improve parenchyma sparing surgeries. In all three categories (i–iii) the treatment phase covered the major part (~ 40%) of the total procedure duration (bottleneck). The simulation results predict that a navigation platform could decrease the total surgery duration by up to 30%.ConclusionThis study showed a DESM based on the analysis of steps during surgical procedures can be used to predict the impact of new technology. SPMs can be used to detect, e.g., the most probable workflow paths which enables predicting next surgical steps, improving surgical training systems, and analyzing surgical performance. Moreover, it provides insight into the points for improvement and bottlenecks in the surgical process.

An Indispensable Revolution For Pakistan, The Beginning Of Robot Assisted MIMVS.

Madam, Minimally invasive surgical (MIS) techniques or ‘sternal sparing’ strategies, performed by making minor right chest incisions, were introduced in the mid-1990s [1,3]. From that point forward, there has been a decrease in surgical injury and postoperative recuperation, coming about in expanded acknowledgement of these approaches. It works on three essential standards: rebuilding and conserving MV pamphlet portability, creating a vast flyer coaptation surface, and remodeling of the mitral annulus to supply an ideal and steady orifice area. [1] The history of MIMVS expanded from the 1990s when minimally invasive approaches such as the parasternal incision, hemi sternal incision and the mini thoracotomy were investigated by autonomous groups led by Delos Cosgrove and Lawrence Cohn. Further advancements include the primary video-directed repair and substitutions performed through a mini-thoracotomy and the primary utilization of end balloon clamping. Later, improvements incorporated the coming of stereoscopic 3D video endoscopy and mechanical surgery. [5] Robotic technology facilitates complex surgical procedures on the mitral valve without opening the chest. The most significant advantage is diminished surgical injury, which deciphers into early recuperation. In 1998, robotic mitral valve repair was initiated and adopted in 2005 by the US following the first multicenter trial. In classic robotic mitral valve surgery, femoral-femoral cardiopulmonary machine cannulation is performed through a small incision in the groin. The surgeon leading the robotic mitral valve surgery must have the relevant heart surgery certification from the country's certifying institution. The console surgeon should be experienced in all aspects of mitral valve surgery and be highly versed in repair techniques as robotic procedures take longer than traditional surgeries, especially during the learning curve. Apprenticeship in the so-called mini-mitral using conventional videoscopy is also an advantage, but not a mandatory requirement The courses on robotic mitral valve surgery including formal didactic and practice-oriented training should be obtained by the lead surgeon and the team. Virtual stimulators and wet labs should be provided by the hospital to the team to practice their first 8-10 cases until they gain expertise ---Continue

Automatic endoscopic navigation based on attention-based network for Nasotracheal Intubation

Nasotracheal intubation (NTI) is one of the most commonly performed procedures in anesthesia and is considered the gold standard for securing the airway of patients. Endoscope operation is critical to the success of NTI. However, this operation remains challenging as it requires the surgeon to classify anatomical landmarks and detect heading targets of the endoscope tip in a sequence of monocular images. To alleviate this problem, this study presents a learning-based navigation method that automatically classifies four different anatomical landmarks and detects the heading target of the endoscope tip from endoscopic images. First, an end-to-end multitask network is introduced that consists of a branch for anatomical landmark classification and another for heading target detection. In addition, a convolutional attention module is designed to improve network performance by combining spatial and channel attention. Second, an endoscopic dataset named intuNav is built for network training. The trained network calculates navigation information without image prior knowledge. Finally, extensive experiments on the built dataset and endoscopic videos demonstrate the high performance of our method, achieving classification (94%) and detection (79.4%) accuracies. The results also indicate that the proposed method is effective and efficient in navigation generation during NTI.

The Clinical and Endoscopic Profiles of Patients With Upper Gastrointestinal Bleeding (UGIB) and the Role of the Rockall Scoring System in Predicting Adverse Outcomes.

Upper gastrointestinal bleeding (UGIB) is one of the common emergencies seen by physicians. Upper gastrointestinal (UGI) endoscopy remains a crucial tool in the identification of UGIB. The aim of the present study was to determine the clinical and endoscopic profiles of UGIB in an adult population. This prospective, cross-sectional study was conducted in Dayanand Medical College and Hospital (DMCH), Ludhiana, where 75 patients aged 18 years and above admitted to the hospital with a history of UGIB from July 1 to December 31, 2018, were enrolled in the study. After obtaining the demographic data, all patients underwent clinical examination, laboratory investigations, and video endoscopy. The Rockall scoring system was used to assess their prognosis. The mean age of the study population was 52.19±6.65 years. Themajority (33%) were in the age group of 51-60 years. Of the study population, 82.7% were male and 17.3% were female. Chronic alcohol intake was found to be the most common risk factor, followed by drug intake. On upper gastrointestinal endoscopy, esophageal varices (65.3%) were the most common finding, followed by peptic ulcer disease (25.2%), gastric erosions (2.6%), gastroduodenitis (1.3%), Mallory-Weiss tear (1.3%), carcinoma stomach (1.3%), Camron's lesion (1.3%), andDieulafoy's lesion (1.3%). Mortality attributed to UGIB was found to be 8%. The present study reported portal hypertension as the most common cause of UGIB, while the most common endoscopic lesions reported were esophageal varices. The factors associated with poor prognosis were age >60 years, shock, respiratory failure, low hemoglobin, low platelet count, deranged international normalized ratio (INR), variceal bleed, renal failure, rebleed, Rockall score ≥ 8, and late endoscopy (>24 hours of admission). Urgent appropriate hospital management definitely helps to reduce morbidity and mortality in patients with UGIB.

Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos

Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved out of the body of patients and out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. This study developed and validated a deep learning model for the identification of out-of-body images in endoscopic videos. The model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries and was externally validated on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC). The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. The model identified out-of-body images with 99.97% ROC AUC on the internal test dataset. Mean ± standard deviation ROC AUC on the multicentric gastric bypass dataset was 99.94 ± 0.07% and 99.71 ± 0.40% on the multicentric cholecystectomy dataset, respectively. The model can reliably identify out-of-body images in endoscopic videos and is publicly shared. This facilitates privacy preservation in surgical video analysis.

Durotomy- and Irrigation-Related Serious Adverse Events During Spinal Endoscopy: Illustrative Case Series and International Surgeon Survey.

Durotomy during endoscopic spine surgery can cause a patient's neurological or cardiovascular status to deteriorate unexpectedly intra- or postoperatively. There is currently limited literature regarding appropriate fluid management strategies, irrigation-related risk factors, and clinical consequences of incidental durotomy during spinal endoscopy, and no validated irrigation protocol exists for endoscopic spine surgery. Thus, the present article sought to (1) describe 3 cases of durotomy, (2) investigate standard epidural pressure measurements, and (3) survey endoscopic spine surgeons on the incidence of adverse effects believed to result from durotomy. The authors first reviewed clinical outcomes and analyzed complications in 3 patients with intraoperatively recognized incidental durotomy. Second, the authors conducted a small case series with intraoperative epidural pressure measurements during gravity-assisted irrigated video endoscopy of the lumbar spine. Measurements were conducted on 12 patients with a transducer assembly that was introduced through the endoscopic working channel of the RIWOSpine Panoview Plus and Vertebris endoscope to the decompression site in the spine. Third, the authors conducted a retrospective, multiple-choice survey of endoscopic spine surgeons to better understand the frequency and seriousness of problems they attributed to irrigation fluid escaping from the surgical decompression site into the spinal canal and neural axis. Descriptive and correlative statistical analyses were performed on the surgeons' responses. In the first part of this study, durotomy-related complications during irrigated spinal endoscopy were observed in 3 patients. Postoperative head computed tomographic (CT) images revealed massive blood in the intracranial subarachnoid space, the basal cisterns, the III and IV ventricle, and the lateral ventricles characteristic of an arterial fisher grade IV subarachnoid hemorrhage, and hydrocephalus without evidence of aneurysms or angiomas. Two additional patients developed intraoperative seizures, cardiac arrhythmia, and hypotension. The head CT image in 1 of these 2 patients had intracranial air entrapment.In the second part, epidural pressure measurements in 12 patients who underwent uneventful routine lumbar interlaminar decompression for L4-L5 and L5-S1 disc herniation showed an average epidural pressure of 24.5 mm Hg.In the third part, the online survey was accessed by 766 spine surgeons worldwide and had a response rate of 43.6%. Irrigation-related problems were reported by 38% of responding surgeons. Only 11.8% used irrigation pumps, with 90% running the pump above 40 mm Hg. Headaches (4.5%) and neck pain (4.9%) were observed by nearly a 10th (9.4%) of surgeons. Seizures in combination with headaches, neck and abdominal pain, soft tissue edema, and nerve root injury were reported by another 5 surgeons. One surgeon reported a delirious patient. Another 14 surgeons thought that they had patients with neurological deficits ranging from nerve root injury to cauda equina syndrome related to irrigation fluid. Autonomic dysreflexia associated with hypertension was attributed by 19 of the 244 responding surgeons to the noxious stimulus of escaped irrigation fluid that migrated from the decompression site in the spinal canal. Two of these 19 surgeons reported 1 case associated with a recognized incidental durotomy and another with postoperative paralysis. Patients should be educated preoperatively about the risk of irrigated spinal endoscopy. Although rare, intracranial blood, hydrocephalus, headaches, neck pain, seizures, and more severe complications, including life-threatening autonomic dysreflexia with hypertension, may arise if irrigation fluid enters the spinal canal or the dural sac and migrates from the endoscopic site along the neural axis rostrally. Experienced endoscopic spine surgeons suspect a correlation between durotomy and irrigation-related extra- and intradural pressure equalization that could be problematic if associated with high volumes of irrigation fluid LEVEL OF EVIDENCE: 3.

Microendoscopic Surgery for Degenerative Disorders of the Cervical and Lumbar Spine: The Influence of the Tubular Workspace on Instrument Angulation, Clinical Outcome, Complications, and Reoperation Rates.

Long-term clinical outcomes with microendoscopic spine surgery (MESS) are poorly investigated. The effect of instrument angulation on clinical outcomes has yet to be assessed. A total of 229 consecutive patients operated on via two MESS systems were analyzed. Instrument angulation for both MESS systems, which differ from each other regarding the working space for instruments, was assessed using a computer model. Patients' charts and endoscopic video recordings were reviewed to determine clinical outcomes, complications, and revision surgery rates. At a minimum follow-up of two years, clinical outcomes were assessed employing the Neck Disability Index (NDI) and Oswestry Disability Index (ODI). A total of 52 posterior cervical foraminotomies (PCF) and 177 lumbar decompression procedures were performed. The mean follow-up was six years (range 2-9 years). At the final follow-up, 69% of cervical and 76% of lumbar patients had no radicular pain. The mean NDI was 10%, and the mean ODI was 12%. PCF resulted in excellent clinical outcomes in 80% of cases and 87% of lumbar procedures. Recurrent disc herniations occurred in 7.7% of patients. The surgical time and repeated procedure rate were significantly lower for the MESS system with increased working space, whereas the clinical outcome and rate of complication were similar. MESS achieves high success rates for treating degenerative spinal disorders in the long term. Increased instrument angulation improves access to the compressive pathology and lowers the surgical time and repeated procedure rate.