Identification Of Landmarks Research Articles

Identification of visible lesions during surveillance endoscopy for Barrett’s esophagus: a video-based survey study

Visible lesion (VL) detection is essential in patients with Barrett's esophagus (BE). We sought to assess the rate of VL detection by academic and community endoscopists using high-definition white-light endoscopy (HD-WLE) and narrow-band imaging (NBI) during surveillance endoscopy. Fifty endoscopists were invited to participate in a prospective video survey study. Participants viewed 25 standardized clips of patients referred for endoscopic therapy. Participants noted identification of anatomic landmarks and VLs using HD-WLE and NBI and reported practice-level data. The criterion standard of VL identification was established by consensus of 5 BE experts. Our primary outcome was the rate of VL identification using HD-WLE and NBI. Forty-four of 50 participants completed the study (22 academic and 22 community). Compared with the criterion standard, participants did not identify 28% (HD-WLE) and 31% (NBI) of VLs. Community endoscopists had more experience (>5 years in practice: community 85% vs academic 54.5%, P= .041; >5 surveillance endoscopies a month: community 85% vs academic 31.8%, P= .046). Across all participants, VL detection using NBI improved significantly with a minimum of 5 surveillance endoscopies per month (area under the curve= .72; 95% confidence interval, .56-.85; P= .006). Despite improved endoscope resolution and availability of virtual chromoendoscopy, the overall rate of VL detection remains low. Identification of VLs using NBI may be volume dependent. Further education and training efforts focused on VL detection during BE surveillance endoscopy are needed.

Machine learning for the automatic assessment of aortic rotational flow and wall shear stress from 4D flow cardiac magnetic resonance imaging.

Three-dimensional (3D) time-resolved phase-contrast cardiac magnetic resonance (4D flow CMR) allows for unparalleled quantification of blood velocity. Despite established potential in aortic diseases, the analysis is time-consuming and requires expert knowledge, hindering clinical application. The present research aimed to develop and test a fully automatic machine learning-based pipeline for aortic 4D flow CMR analysis. Four hundred and four subjects were prospectively included. Ground-truth to train the algorithms was generated by experts. The cohort was divided into training (323 patients) and testing (81) sets and used to train and test a 3D nnU-Net for segmentation and a Deep Q-Network algorithm for landmark detection. In-plane (IRF) and through-plane (SFRR) rotational flow descriptors and axial and circumferential wall shear stress (WSS) were computed at ten planes covering the ascending aorta and arch. Automatic aortic segmentation resulted in a median Dice score (DS) of 0.949 and average symmetric surface distance of 0.839 (0.632-1.071) mm, comparable with the state of the art. Aortic landmarks were located with a precision comparable with experts in the sinotubular junction and first and third supra-aortic vessels (p = 0.513, 0.592 and 0.905, respectively) but with lower precision in the pulmonary bifurcation (p = 0.028), resulting in precise localisation of analysis planes. Automatic flow assessment showed excellent (ICC > 0.9) agreement with manual quantification of SFRR and good-to-excellent agreement (ICC > 0.75) in the measurement of IRF and axial and circumferential WSS. Fully automatic analysis of complex aortic flow dynamics from 4D flow CMR is feasible. Its implementation could foster the clinical use of 4D flow CMR. • 4D flow CMR allows for unparalleled aortic blood flow analysis but requires aortic segmentation and anatomical landmark identification, which are time-consuming, limiting 4D flow CMR widespread use. • A fully automatic machine learning pipeline for aortic 4D flow CMR analysis was trained with data of 323 patients and tested in 81 patients, ensuring a balanced distribution of aneurysm aetiologies. • Automatic assessment of complex flow characteristics such as rotational flow and wall shear stress showed good-to-excellent agreement with manual quantification.

Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot

Landmark detection in flatfoot radiographs is crucial in analyzing foot deformity. Here, we evaluated the accuracy and efficiency of the automated identification of flatfoot landmarks using a newly developed cascade convolutional neural network (CNN) algorithm, Flatfoot Landmarks AnnoTating Network (FlatNet). A total of 1200 consecutive weight-bearing lateral radiographs of the foot were acquired. The first 1050 radiographs were used as the training and tuning, and the following 150 radiographs were used as the test sets, respectively. An expert orthopedic surgeon (A) manually labeled ground truths for twenty-five anatomical landmarks. Two orthopedic surgeons (A and B, each with eight years of clinical experience) and a general physician (GP) independently identified the landmarks of the test sets using the same method. After two weeks, observers B and GP independently identified the landmarks once again using the developed deep learning CNN model (DLm). The X- and Y-coordinates and the mean absolute distance were evaluated. The average differences (mm) from the ground truth were 0.60 ± 0.57, 1.37 ± 1.28, and 1.05 ± 1.23 for the X-coordinate, and 0.46 ± 0.59, 0.97 ± 0.98, and 0.73 ± 0.90 for the Y-coordinate in DLm, B, and GP, respectively. The average differences (mm) from the ground truth were 0.84 ± 0.73, 1.90 ± 1.34, and 1.42 ± 1.40 for the absolute distance in DLm, B, and GP, respectively. Under the guidance of the DLm, the overall differences (mm) from the ground truth were enhanced to 0.87 ± 1.21, 0.69 ± 0.74, and 1.24 ± 1.31 for the X-coordinate, Y-coordinate, and absolute distance, respectively, for observer B. The differences were also enhanced to 0.74 ± 0.73, 0.57 ± 0.63, and 1.04 ± 0.85 for observer GP. The newly developed FlatNet exhibited better accuracy and reliability than the observers. Furthermore, under the FlatNet guidance, the accuracy and reliability of the human observers generally improved.

Comparison between a human judge and automatic landmark identification on digital models

Accurate landmark identification is a prerequisite for accurate and reliable biomedical image analysis. Orthodontic study models are valuable tools for diagnosis, treatment planning, and maintaining complete records. The purpose of this study was to evaluate the reliability and validity of a software program (Align Technology, Inc) as a tool for automatic landmark location. Using digital intraoral scans of 10 dental arches, 4 calibrated human judges independently located cusp tips and interproximal contacts. The same landmarks were automatically identified by the software. Intraclass correlation coefficient (Cronbach α), absolute mean errors, and regression analysis were calculated. In addition, Bland-Altman 95% confidence limits were also applied to the data to graphically display agreement on landmark identification between the human judges and the software. The intraclass correlation coefficient between the software and the human judges' average for the x-, y-, and z-coordinates for all landmarks was excellent, at 1.0, 1.0, and 0.98, respectively. The regression analysis and Bland-Altman plots show no systematic errors for agreement on landmark identification between the human judges and the software. Landmark location was nearly identical between the software and the human judges, making the methods interchangeable.

A methodology for task-specific metrological characterization of low-cost 3D camera for face analysis

Facial appearance is one prominent feature in analyzing several aspects, e.g., aesthetics and expression of emotions, and face analysis is crucial in many fields. Face analysis requires measurements that can be performed by different technologies and typically relies on landmarks identification. Recently, low-cost customer grade 3D cameras have been introduced in the market, enabling an increase of application at affordable cost with nominal adequate performances. Novel cameras require to be thoroughly metrologically characterized to guarantee these performances. Cameras are calibrated following a standard general-purpose procedure. However, the specificity of facial measurements requires a task-based metrological characterization to include typical influence factors. This work outlines a methodology for task-based metrological characterization of low-cost 3D cameras for facial analysis, consisting of: influence factor identification by ANOVA, related uncertainty contribution assessment, uncertainty propagation, landmarking uncertainty estimation. The proposed methodology is then demonstrated on a customer grade state-of-the-art 3D camera available on the market.

Robotically assisted augmented reality system for identification of targeted lymph nodes in laparoscopic gynecological surgery: a first step toward the identification of sentinel node : Augmented reality in gynecological surgery.

To prove feasibility of multimodal and temporal fusion of laparoscopic images with preoperative computed tomography scans for a real-time in vivo-targeted lymph node (TLN) detection during minimally invasive pelvic lymphadenectomy and to validate and enable such guidance for safe and accurate sentinel lymph node dissection, including anatomical landmarks in an experimental model. A measurement campaign determined the most accurate tracking system (UR5-Cobot versus NDI Polaris). The subsequent interventions on two pigs consisted of an identification of artificial TLN and anatomical landmarks without and with augmented reality (AR) assistance. The AR overlay on target structures was quantitatively evaluated. The clinical relevance of our system was assessed via a questionnaire completed by experienced and trainee surgeons. An AR-based robotic assistance system that performed real-time multimodal and temporal fusion of laparoscopic images with preoperative medical images was developed and tested. It enabled the detection of TLN and their surrounding anatomical structures during pelvic lymphadenectomy. Accuracy of the CT overlay was > 90%, with overflow rates < 6%. When comparing AR to direct vision, we found that scores were significatively higher in AR for all target structures. AR aided both experienced surgeons and trainees, whether it was for TLN, ureter, or vessel identification. This computer-assisted system was reliable, safe, and accurate, and the present achievements represent a first step toward a clinical study.

Technological trajectory based on micro level technological capability: Evidence from the Brazilian rice industry

This study examines technological capability accumulation and the Brazilian rice industry's technological trajectory. Multiple case design was chosen, with an analysis of seven firms involved in the Brazilian rice industry. The results indicate three factors: The first was the identification of technological landmarks for firms in this sector (peeling and polishing, electronic grain selection, electronic packaging, use of forklifts and palletizers, and Industry 4.0). The second is related to the fact that different firms implemented innovative activities and technologies at different speeds, originating from the sector's different technological trajectories. The third is associated with identifying activities within the Brazilian rice industry that can be generalized to other food industries. These results have implications for formulating public policies in the sector, such as creating producer support mechanisms (research and technology transfer institutions), qualification of professionals, and encouragement for economic efficiency and competition.

Automatic landmark identification for surgical 3d-navigation- A proposed method for marker-free dental surgical navigation systems.

This paper proposes a conceptual method to calculate the pose of a stereo-vision camera relative to an artificial mandible without additional markers. The general method for marker-free navigation has four steps: 1) parallel image acquisition by a stereo-vision camera, 2) automatic identification of 2d point pairs (landmark pairs) in a left and a right image, 3) calculation of related 3d points in the joint camera coordinate system and 4) matching of 3d points generated to a preoperative 3d model (i.e.,CT data based). To identify and compare landmarks in the acquired stereo images, well-known algorithms for landmark detection, description and matching were compared within the developed approach. Finally, the BRISK algorithm (Leutenegger S, Chli M, Siegwart RY. BRISK: Binary Robust invariant scalable keypoints. Proceedings of the IEEE International Conference on Computer Vision; 2011: 2548-2555) was used. The proposed method was implemented in MATLAB® and validated in vitro with one artificial mandible. The accuracy evaluation of the camera positions calculated resulted in an average deviation error of 1.45mm±0.76mm to the real camera displacement. This value was calculated using only stereo images with over 100 reconstructed landmark pairs each. This provides the basis for marker-free navigation.

Is each horse unique and unrepeatable? An explanation through morphometric and statistical analysis of the limb bones.

Horses went through functional differentiation during their evolutionary and reproductive development, however, some body parts continued to improve their specificity for speed. Regarding this, we evaluated by morphometric analysis the relationship between limb section (LS) differences in the appendicular structures of horses. Two hundred and seven (n=207) adult horses of different breeds and sex were selected. The limb section was measured by identification of topographic palpable skeleton landmarks with a measurement tape (centimetres). Quantitative variables (limb section) were analysed with PAST Paleontological Statistics Version 3.16. Spearman's ordinal or non-parametric correlation coefficient was used to study the relationship between the limb section of each variable (p < 0.05), with a low coefficient of variation (<30%). In 45 analysed correlations, 35 were statistically significant and 10 were discarded because p > 0.05. These results show that most variables were correlated with each other. As a result, we can imply that some LS of the horse's limbs are correlated with each other, positively or negatively and to a different degree. In conclusion, the correlations between LS lengths determine the size and shape of each horse, making individual horses unique and unrepeatable.

Predicting the Position of the Internal Landmarks of Middle Cranial Fossa Using the Zygomatic Root: An Attempt to Simplify Its Complexity

AbstractSubtemporal–extradural middle cranial fossa (MCF) surgical approach is used to access pathologies involving anterior or posterior part of the petrous bone or its apex. A reliable and precise identification of the important internal landmarks is key to a safe surgery with decreased incidence of morbidity. The zygomatic root (ZR) serves as a reliable reference guide for the surgeon when navigating through the MCF. The aim of the study is to establish an association between the extent of the ZR to the key internal foramina and bony prominences in lateral fossa of the MCF to help the neurosurgeon to safely navigate through the maze of structures of the MCF. The study demonstrates that the ZR is a reliable marker to estimate and predict the position of foramen ovale, foramen spinosum, and trigeminal fossa but not for the position of hiatus of greater petrosal nerve or the arcuate eminence. Successful localization of the foramen ovale, spinosum, and trigeminal fossa would reduce intraoperative time, ensure lesser retraction of brain, and hence reduce patient morbidity while performing surgeries on lesions of/in the internal acoustic canal, petroclival junctions, cerebellopontine angles, basilar artery, or transovale cannulation for the treatment of trigeminal neuralgia.

Analyzing Force-Time Curves: Comparison of Commercially Available Automated Software and Custom MATLAB Analyses.

Merrigan, JJ, Stone, JD, Galster, SM, and Hagen, JA. Analyzing force-time curves: Comparison of commercially available automated software and custom MATLAB analyses. J Strength Cond Res 36(9): 2387-2402, 2022-With the growing prevalence of commercial force plate solutions providing automated force-time curve analysis, it is critical to understand the level of agreement across techniques. Thus, this study directly compared commercial and custom software analyses across force-time curves. Twenty-four male and female subjects completed 6 trials of countermovement, squat, and drop jumps, and isometric mid-thigh pulls on the same force plate. Vertical ground reaction forces were analyzed by automated software from Vald Performance, Hawkin Dynamics, and custom MATLAB scripts. Trials were visually assessed to verify proper landmark identifications. Systematic and proportional bias among analyses were compared via least products regressions, Bland-Altman plots, and percent error. Hawkin Dynamics had subtle differences in analysis procedures and demonstrated low percent errors across all tests (<3% error), despite demonstrating systematic and proportional bias for several metrics. ForceDecks demonstrated larger percent differences and greater biases for several metrics. These errors likely result from different identification of movement initiation, system weight, and integration techniques, which causes error to subsequent landmark identifications (e.g., braking/propulsive phases) and respective force-time metrics. Many metrics were in agreement between devices, such as isometric mid-thigh pull peak force consistently within 1 N across analyses, but some metrics are difficult and incomparable across software analyses (i.e., rate of force development). Overall, many metrics were in agreement across each commercial software and custom MATLAB analyses after visually confirming landmarks. However, because of inconsistencies, it is important to only compare metrics that are in agreement across software analyses when absolutely necessary.

Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery.

ObjectiveTo investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery.MethodsA total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed.ResultsThe total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups.ConclusionsThe CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Artificial intelligence (AI) is a branch of science concerned with developing programs and computers that can gather data, reason about it, and then translate it into intelligent actions. AI is a broad area that includes reasoning, typical linguistic dispensation, machine learning, and planning. In the area of medicine and dentistry, machine learning is currently the most widely used AI application. This narrative review is aimed at giving an outline of cephalometric analysis in orthodontics using AI. Latest algorithms are developing rapidly, and computational resources are increasing, resulting in increased efficiency, accuracy, and reliability. Current techniques for completely automatic identification of cephalometric landmarks have considerably improved efficiency and growth prospects for their regular use. The primary considerations for effective orthodontic treatment are an accurate diagnosis, exceptional treatment planning, and good prognosis estimation. The main objective of the AI technique is to make dentists' work more precise and accurate. AI is increasingly being used in the area of orthodontic treatment. It has been evidenced to be a time-saving and reliable tool in many ways. AI is a promising tool for facilitating cephalometric tracing in routine clinical practice and analyzing large databases for research purposes.

Evaluation of the accuracy and reliability of WebCeph – An artificial intelligence-based online software

Objectives: Landmark identification is of utmost importance in cephalometric analysis but it turns out to be the main source of error. With modern inventions in the field of artificial intelligence (AI), it becomes essential to assess the reliability of computer-automated programs. A greater deal of time can be conserved with fully automated programs such as WebCeph, which uses an AI-based algorithm that performs automated and immediate cephalometric analysis. This study aimed to evaluate the accuracy, reliability, and duration of tracing cephalometric radiographs with WebCeph, an AI-based software in comparison to digital tracing with FACAD and manual tracing. The null hypothesis proposed is that there is no statistically significant difference among the three methods with regard to accuracy of cephalometric analysis. Material and Methods: Pre-treatment cephalometric radiographs of 25 patients (14 males and 11 females, mean age of 18 ± 3.2 years) were selected randomly from the dental information archiving software of Saveetha University, Department of Orthodontics, Chennai. Composite analysis with skeletal, dental and soft-tissue parameters was selected and cephalometric analysis was done with all three methods – Manual tracing (Group 1), digital tracing using FACAD (Group 2), and fully automated AI-based software WebCeph (Group 3). The timing for each method of analysis was calculated using a stopwatch in seconds. Values were tabulated in an Excel sheet and statistical analysis including one-way analysis of variance and post hoc Tukey test were performed. Results: No statistically significant difference was found between the three methods for cephalometric analysis, P &gt; 0.05. The time taken for measurement using the three different methods was the least while using WebCeph (30.2 ± 6.4 s) and the maximum while manual tracing (472 ± 40.4 s). Conclusion: WebCeph is a reliable, faster and practical tool for analyzing cephalometric analysis in comparison to digital tracing using FACAD and manual tracing.

Automated landmark identification on cone-beam computed tomography: Accuracy and reliability.

To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges. A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated. Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range. Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.

Assessment of esophagogastroduodenoscopy skills on simulators before real-life performance.

Background and study aims Operator competency is essential for esophagogastroduodenoscopy (EGD) quality, which makes appropriate training with a final test important. The aims of this study were to develop a test for assessing skills in performing EGD, gather validity evidence for the test, and establish a credible pass/fail score. Methods An expert panel developed a practical test using the Simbionix GI Mentor II simulator (3 D Systems) and an EGD phantom (OGI 4, CLA Medical) with a diagnostic (DP) and a technical skills part (TSP) for a prospective validation study. During the test a supervisor measured: 1) total time; 2) degree of mucosal visualization; and 3) landmarks and pathology identification. The contrasting groups standard setting method was used to establish a pass/fail score. Results We included 15 novices (N), 10 intermediates (I), and 10 experienced endoscopists (E). The internal structure was high with a Cronbach’s alpha of 0.76 for TSP time consumption and 0.74 for the identification of landmarks. Mean total times, in minutes, for the DP were N 15.7, I 11.3, and E 7.0, and for TSP., they were N 7.9, I 8.9, and E 2.9. The total numbers of identified landmarks were N 26, I 41, and E 48. Mean visualization percentages were N 80, I 71, and E 71. A pass/fail standard was established requiring identification of all landmarks and performance of the TSP in < 5 minutes. All experienced endoscopists passed, while none of the endoscopists in the other categories did.Conclusions We established a test that can distinguish between participants with different competencies. This enables an objective and evidence-based approach to assessment of competencies in EGD.

Face Image Animation with Adversarial Learning and Motion Transfer

Significant advances have been made in facial image animation from a single image. Nonetheless, generating convincing facial feature movements remains a complex challenge in computer graphics. The purpose of this study is to develop an efficient and effective approach for transferring motion from a source video to a single facial image by governing the position and expression of the face in the video to generate a new video imitating the source image. Compared to prior methods that focus solely on manipulating facial expressions, this model has been trained to distinguish the moving foreground from the background image and to create motions such as facial rotation and translation as well as small local motions such as gaze shift. The pro-posed technique uses generative adversarial networks GANs with a motion transfer model. The network forecasts photo-realistic video frames for a given target image using synthetic input in renderings from a parametric face model. The authenticity in this postprocessing conversion is attained by precise image manipulation. Thorough adversarial training is used to produce greater accuracy in this postprocessing conver-sion. Although more improvements to face landmark identification on videos and face super-resolution techniques have been made to improve the results, the pro-posed technique can provide more coherent videos with improved visual quality, resulting in more aligned landmark sequences for training. In addition, experiments indicate that we obtain superior results compared to those obtained by the state-of-the-art image-driven technique with PSNR 30.74 and SSIM 0.90

V05-03 NEAR MISS IN UROLOGICAL SURGERY: DEFINITION AND TIPS FOR PREVENTING UNEXPECTED COMPLICATIONS

You have accessJournal of UrologyCME1 May 2022V05-03 NEAR MISS IN UROLOGICAL SURGERY: DEFINITION AND TIPS FOR PREVENTING UNEXPECTED COMPLICATIONS Stefano Parodi, Stefano Tappero, Giovanni Guano, Andrea Pacchetti, Francesco Chierigo, Guglielmo Mantica, Marco Borghesi, Nazareno Suardi, and Carlo Terrone Stefano ParodiStefano Parodi More articles by this author , Stefano TapperoStefano Tappero More articles by this author , Giovanni GuanoGiovanni Guano More articles by this author , Andrea PacchettiAndrea Pacchetti More articles by this author , Francesco ChierigoFrancesco Chierigo More articles by this author , Guglielmo ManticaGuglielmo Mantica More articles by this author , Marco BorghesiMarco Borghesi More articles by this author , Nazareno SuardiNazareno Suardi More articles by this author , and Carlo TerroneCarlo Terrone More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002579.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: A surgical near miss (NM) is defined as an unintentional incident that could have caused damage to the patient but was narrowly avoided because of pure chance or because it was intercepted. In this video we aim to depict a clear example of NM during a left robot-assisted partial nephrectomy (PN), having potentially consisted of wrong renal artery clamping. METHODS: We present the case of a 66-year-old woman submitted to left PN at a tertiary care referral centre for a 55 mm upper pole left renal mass (SPARE 9), cT1-cN0M0. The preoperatory CT scan showed a single renal artery on both sides.Preoperative Hb and GFR were 14.9 mg/dl and 53 ml/min, respectively. The Da Vinci Xi robot was employed. The fist operator was a skilled laparoscopic/robotic surgeon (≥1000 procedures). The patient was placed in right flank position. Three 8-mm trocars for the 30° camera and the operative robotic arms and 2 trocars for the assistant were placed. At the abdominal exploration a high amount of visceral fat was found. Once incised the peritoneum at the level of the Toldt line and medialized the descending colon, a direct access to renal vessels was performed.In the specific case, after the identification of left renal vein and renal artery, the latter was underpassed with a vessel loop. Before starting the isolation of renal mass a careful identification of the aorta was attempted. However, after exposure of the left gonadal vein, the aorta was identified laterally to the isolated renal artery. Therefore, the surgeon realized that the isolated artery was the right renal one. The vessel loop was removed, the left renal artery identified and isolated and the PN with warm ischemia was performed. RESULTS: Operative time was 240 min, estimated blood loss was 300 ml and no complications occurred. Final pathology revealed a clear cell RCC with eosinophilic variant, pT1b-pNx-R0.Follow-up period was uneventful and 2-y postoperative GFR was 52 ml/min. After a specific surgical audit the predisposing factors for our NM were identified: suboptimal evaluation of preoperative CT scan, no 3D reconstructions available, abundant visceral fat, left-sided surgery and direct access to the renal pedicle without identification of gonadal vein. CONCLUSIONS: Procedural errors in the OR may come along with tremendous complications for the patient. The identification of the anatomical landmarks is mandatory and the discussion of the potential NM helps the prevention of adverse dangerous events. In our case, failure to recognize the error would result in serious complications, especially if a radical nephrectomy had been planned. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e516 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Stefano Parodi More articles by this author Stefano Tappero More articles by this author Giovanni Guano More articles by this author Andrea Pacchetti More articles by this author Francesco Chierigo More articles by this author Guglielmo Mantica More articles by this author Marco Borghesi More articles by this author Nazareno Suardi More articles by this author Carlo Terrone More articles by this author Expand All Advertisement PDF DownloadLoading ...

Suture lateralization in congenital bilateral vocal cord immobility in neonates and infants: A hybrid approach

IntroductionBilateral vocal cord immobility (BVCI) is a leading cause of stridor and airway obstruction in neonates which may arise idiopathically, as a result of birth trauma, or in the setting of Central nervous system lesions such as Arnold-Chiari malformation. Although many children with BVCI may be managed conservatively, surgical intervention may be necessary in those patients with ongoing upper airway obstruction. Many interventions have been described including, tracheostomy, cordotomy, cricoid splitting procedures, and arytenoidectomy. Vocal cord suture lateralization has the advantage of being both reversible and less morbid than other surgical interventions for BVCI. This study describes a novel variation of the suture lateralization procedure which aids in correct placement of the suture thus minimizing multiple needle passes and associated laryngeal edema. MethodsA retrospective chart review was undertaken of five patients undergoing suture lateralization of the vocal cord for BVCI at a single institution over a 10 year period. Post-operative voice and swallow outcomes were recorded. ResultsOf five patients undergoing this procedure four returned to normal diet. One was diagnosed with a progressive neurological disorder and was persistently fed via gastrostomy tube. Three of the five patients spontaneously recovered vocal cord function and went on to have normal voice after suture removal. ConclusionSuture lateralization is a safe and effective means of alleviating upper airway obstruction in BVCI. It has the advantages of being reversable and minimally invasive; however, optimal suture placement is both vital and challenging. The novel technique described in this study allows identification of landmarks as and aid to suture placement thus reducing the need for multiple needle passes into the lumen of the larynx.

Brainstem Pilocytic Astrocytoma, a Surgical Disease: 2-Dimensional Operative Video.

Brainstem pilocytic astrocytomas are benign lesions that frequently develop in the medulla with an often exophytic component.1-3 These lesions develop in children and young adults and are revealed by lower cranial nerves dysfunction and long tracts involvement. Medulla pilocytic astrocytomas have a better overall prognosis than pontine and midbrain lesions, particularly after surgical resection, which can be curative, allowing the control of the disease and avoiding adjuvant treatments in young patients.2-5 The transcondylar approach with mobilization of the vertebral artery offers the necessary ventral exposure for the resection of such medulla oblongata lesions.6 After exposure, the initial step of resection starts with the cerebellopontine angle occupying exophytic portion after identification of cerebrovascular landmarks. Ultrasonic aspiration on a low setting allows to progressively debulk the tumor and identify the normal medulla tissue while closely following the electrophysiological neuromonitoring. Preservation of the small medulla perforators is the key for successful resection, including minimal use of bipolar coagulation in the vicinity of brainstem tissue. We demonstrate these principles through the case of a 33-year-old woman presenting with a large left medulla oblongata exophytic tumor, with an MRI presentation consistent with pilocytic astrocytoma. The patient consented to the procedure and publication of her images.