Automatic Detection and Tracking of Anatomical Landmarks in Transesophageal Echocardiography for Quantification of Left Ventricular Function.

OBJECTIVE: Evaluation of left ventricular (LV) function in critical care patients is useful for guidance of therapy and early detection of LV dysfunction, but the tools currently available are too time-consuming. To resolve this issue, we previously proposed a method for the continuous and automatic quantification of global LV function in critical care patients based on the detection and tracking of anatomical landmarks on transesophageal heart ultrasound. In the present study, our aim was to improve the performance of mitral annulus detection in transesophageal echocardiography (TEE). METHODS: We investigated several state-of-the-art networks for both the detection and tracking of the mitral annulus in TEE. We integrated the networks into a pipeline for automatic assessment of LV function through estimation of the mitral annular plane systolic excursion (MAPSE), called autoMAPSE. TEE recordings from a total of 245 patients were collected from St. Olav's University Hospital and used to train and test the respective networks. We evaluated the agreement between autoMAPSE estimates and manual references annotated by expert echocardiographers in 30 Echolab patients and 50 critical care patients. Furthermore, we proposed a prototype of autoMAPSE for clinical integration and tested it in critical care patients in the intensive care unit. RESULTS: Compared with manual references, we achieved a mean difference of 0.8 (95% limits of agreement: -2.9 to 4.7) mm in Echolab patients, with a feasibility of 85.7%. In critical care patients, we reached a mean difference of 0.6 (95% limits of agreement: -2.3 to 3.5) mm and a feasibility of 88.1%. The clinical prototype of autoMAPSE achieved real-time performance. CONCLUSION: Automatic quantification of LV function had high feasibility in clinical settings. The agreement with manual references was comparable to inter-observer variability of clinical experts.

BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition.

Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset's quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.

Divergence between CBCT and Optical Scans for Soft Tissue Analysis and Cephalometry in Facial Imaging: A cross-sectional study on healthy adults.

BACKGROUND: Facial soft tissue analysis is becoming increasingly emphasized in orthodontic diagnosis and treatment planning. While traditional cephalometry primarily focuses on hard tissues, recent non-invasive imaging techniques offer the potential to comprehensively evaluate three-dimensional (3D) facial soft tissues. The aim of the study was to establish the geometrical 3D and cephalometric divergence between Cone Beam Computed Tomography (CBCT) derived images and scanned soft tissues. Crucial for enhancing orthodontic diagnosis, minimizing patient exposure to ionizing radiation and providing facial cephalometric parameters. MATERIAL AND METHODS: A cross-sectional study was conducted from January 2020 to May 2023. CBCT and 3D facial scans were obtained simultaneously using a specialized imaging system. Reproducible landmark points were selected for both cephalometric and soft tissue analysis. Angular and linear measurements were recorded, and correlations between CT and facial scans were statistically assessed. RESULTS: Comparisons between 10 CBCT-derived and 10 facial scan-based soft tissue representations resulted into 1.8mm mean root median square (RMS). Angular measurements, such as ANB, right gonial angle, and left gonial angle, exhibited a 0.9° of difference with their respective soft tissue variables. In contrast, linear measurements of total anterior facial height showed a lower correlation coefficient, equal to 0.51. The correlation between soft tissues and underlying hard tissues was more pronounced for gonial angles. CONCLUSION: Facial soft tissue analysis using either 3D facial scans or CBCT-derived offers similar results for orthodontic diagnosis and treatment planning. These findings support the use of non-invasive diagnostic tools in orthodontics, although further investigations are needed to comprehensively understand the complexity of hard and soft tissue relationships.

Automated localization of mandibular landmarks in the construction of mandibular median sagittal plane.

OBJECTIVE: To use deep learning to segment the mandible and identify three-dimensional (3D) anatomical landmarks from cone-beam computed tomography (CBCT) images, the planes constructed from the mandibular midline landmarks were compared and analyzed to find the best mandibular midsagittal plane (MMSP). METHODS: A total of 400 participants were randomly divided into a training group (n = 360) and a validation group (n = 40). Normal individuals were used as the test group (n = 50). The PointRend deep learning mechanism segmented the mandible from CBCT images and accurately identified 27 anatomic landmarks via PoseNet. 3D coordinates of 5 central landmarks and 2 pairs of side landmarks were obtained for the test group. Every 35 combinations of 3 midline landmarks were screened using the template mapping technique. The asymmetry index (AI) was calculated for each of the 35 mirror planes. The template mapping technique plane was used as the reference plane; the top four planes with the smallest AIs were compared through distance, volume difference, and similarity index to find the plane with the fewest errors. RESULTS: The mandible was segmented automatically in 10 ± 1.5 s with a 0.98 Dice similarity coefficient. The mean landmark localization error for the 27 landmarks was 1.04 ± 0.28 mm. MMSP should use the plane made by B (supramentale), Gn (gnathion), and F (mandibular foramen). The average AI grade was 1.6 (min-max: 0.59-3.61). There was no significant difference in distance or volume (P > 0.05); however, the similarity index was significantly different (P < 0.01). CONCLUSION: Deep learning can automatically segment the mandible, identify anatomic landmarks, and address medicinal demands in people without mandibular deformities. The most accurate MMSP was the B-Gn-F plane.

The accuracy and reliability of different midsagittal planes in the symmetry assessment using cone-beam computed tomography.

Symmetry is an essential component of esthetic assessment. Accurate assessment of facial symmetry is critical to the treatment plan of orthognathic surgery and orthodontic treatment. However, there is no internationally accepted midsagittal plane (MSP) for orthodontists and orthognathic surgeons. The purpose of this study was to explore a clinically friendly MSP, which is more accurate and reliable than what is commonly used in symmetry assessment. Forty patients with symmetric craniofacial structures were analyzed on cone-beam computed tomography (CBCT) scans. The CBCT data were exported to the Simplant Pro software to build four reference planes that were constructed by nasion (N), basion (Ba), sella (S), odontoid (Dent), or incisive foramen (IF). A total of 31 landmarks were located to determine which reference plane is the most optimal MSP by comparing the asymmetry index (AI). The mean value of AI showed a significant difference (p < 0.05) among four reference planes. Also, the mean value of AI for all landmarks showed that Plane 2 (consisting of N, Ba, and IF) and Plane 4 (consisting of N, IF, and Dent) were more accurate and stable. In conclusion, the MSP consisting of N, Dent, and IF shows more accuracy and reliability than the other planes. Further, it is more clinically friendly because of its significant advantage in landmarking.

Identifying anatomic landmarks and median nerve characteristics for the analysis of persistent carpal tunnel syndrome using magnetic resonance imaging (MRI).

OBJECTIVE: Carpal tunnel syndrome (CTS) is the most common nerve entrapment neuropathy in the USA. In this study, we define anatomical landmarks to assess symptomatic and asymptomatic cohorts with persistent CTS using MRI imaging. MATERIALS AND METHODS: Distal vs proximal incomplete release was determined using the distal most aspect of the hook of hamate and the distal wrist crease. An incomplete release showed the transverse carpal ligament (TCL) intact at either boundary. Twenty-one patients with persistent CTS were analyzed for incomplete release, median nerve enlargement and T2 signal hyperintensity, and flattening ratio using postoperative wrist MRI. These findings were compared to a ten-patient asymptomatic persistent CTS control group. Fisher's exact and a Student's two-tailed t-tests were used to determine statistical significance. RESULTS: In the symptomatic persistent CTS group, 13 (61.9%) incomplete releases were identified, 5 (38.5%) incomplete distally, and 1 (7.7%) incomplete proximally. There was no statistical significance in the rate of incomplete releases when compared to the asymptomatic group (p = 1.00). T2 signal hyperintensity and enlargement at the site of release showed no statistical significance, (p = 0.319 and p = 0.999, respectively). The mean flattening ratio at the site of release in the symptomatic group (2.45 ± 0.7) was statistically significant compared to the asymptomatic group (1.48 ± 0.46), (p = 0.007). CONCLUSION: Utilizing the established landmarks, the full length of the TCL can be assessed via MRI. Additionally, evaluation of the median nerve flattening ratio at the level of the incomplete release may be utilized as an aid to the clinical management of persistent CTS.

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population.

BACKGROUND: Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. METHODS: A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. RESULTS: A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). CONCLUSIONS: The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.

Automatic landmark identification in cone-beam computed tomography.

OBJECTIVE: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans. MATERIALS AND METHODS: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position. RESULTS: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU. CONCLUSION: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.

A semi-supervised learning approach for automated 3D cephalometric landmark identification using computed tomography.

Identification of 3D cephalometric landmarks that serve as proxy to the shape of human skull is the fundamental step in cephalometric analysis. Since manual landmarking from 3D computed tomography (CT) images is a cumbersome task even for the trained experts, automatic 3D landmark detection system is in a great need. Recently, automatic landmarking of 2D cephalograms using deep learning (DL) has achieved great success, but 3D landmarking for more than 80 landmarks has not yet reached a satisfactory level, because of the factors hindering machine learning such as the high dimensionality of the input data and limited amount of training data due to the ethical restrictions on the use of medical data. This paper presents a semi-supervised DL method for 3D landmarking that takes advantage of anonymized landmark dataset with paired CT data being removed. The proposed method first detects a small number of easy-to-find reference landmarks, then uses them to provide a rough estimation of the all landmarks by utilizing the low dimensional representation learned by variational autoencoder (VAE). The anonymized landmark dataset is used for training the VAE. Finally, coarse-to-fine detection is applied to the small bounding box provided by rough estimation, using separate strategies suitable for the mandible and the cranium. For mandibular landmarks, patch-based 3D CNN is applied to the segmented image of the mandible (separated from the maxilla), in order to capture 3D morphological features of mandible associated with the landmarks. We detect 6 landmarks around the condyle all at once rather than one by one, because they are closely related to each other. For cranial landmarks, we again use the VAE-based latent representation for more accurate annotation. In our experiment, the proposed method achieved a mean detection error of 2.88 mm for 90 landmarks using only 15 paired training data.

3D Facial Landmark Localization for cephalometric analysis.

Cephalometric analysis is an important and routine task in the medical field to assess craniofacial development and to diagnose cranial deformities and midline facial abnormalities. The advance of 3D digital techniques potentiated the development of 3D cephalometry, which includes the localization of cephalometric landmarks in the 3D models. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra/inter-observer variability. In this paper, a framework to automatically locate cephalometric landmarks in 3D facial models is presented. The landmark detector is divided into two stages: (i) creation of 2D maps representative of the 3D model; and (ii) landmarks' detection through a regression convolutional neural network (CNN). In the first step, the 3D facial model is transformed to 2D maps retrieved from 3D shape descriptors. In the second stage, a CNN is used to estimate a probability map for each landmark using the 2D representations as input. The detection method was evaluated in three different datasets of 3D facial models, namely the Texas 3DFR, the BU3DFE, and the Bosphorus databases. An average distance error of 2.3, 3.0, and 3.2 mm were obtained for the landmarks evaluated on each dataset. The obtained results demonstrated the accuracy of the method in different 3D facial datasets with a performance competitive to the state-of-the-art methods, allowing to prove its versability to different 3D models. Clinical Relevance- Overall, the performance of the landmark detector demonstrated its potential to be used for 3D cephalometric analysis.

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning.

The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set (n = 160) and a test set (n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator(n = 178) or twice by 3 operators (n = 20, test set only). After inference on the test set, 1 CT scan showed "very low" confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were -0.3 ± 1.3° and -0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland-Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

Ultrasound Evaluation of Retrocervical and Parametrial Deep Endometriosis on the Basis of Surgical Anatomic Landmarks.

STUDY OBJECTIVES: To assess the value of combined transvaginal/transabdominal ultrasonographic evaluation performed by experienced examiners for deep infiltrating endometriosis (DIE) lesions of the retrocervical (torus uterinus and uterosacral ligaments) and parametrial areas and summarize the features and anatomic criteria for identification of these lesions and their extent in the above-mentioned pelvic compartments. DESIGN: Retrospective study. SETTING: A specialized endometriosis center in Avellino, Italy. PATIENTS: A retrospective cohort of patients who underwent laparoscopic surgery for clinically suspected DIE between January 1, 2014, and December 31, 2018, with a dedicated ultrasound (US) evaluation performed no more than 1 month before the intervention. INTERVENTIONS: Preoperative US findings and surgical reports were reviewed. Using the findings of laparoscopic surgery as the gold standard, the sensitivity and specificity of preoperative US evaluation for retrocervical and parametrial endometriotic lesions were calculated with the corresponding 95% confidence intervals. MEASUREMENTS AND MAIN RESULTS: A total of 4983 patients were included. US evaluation showed high diagnostic accuracy for DIE detection in the examined pelvic compartments, with sensitivity and specificity of 97% to 98% and 98% to 100%, respectively, for both retrocervical (torus uterinus and uterosacral ligaments insertion) and parametrial lesions. CONCLUSION: Parametrial extension of DIE indicates major surgical technical difficulties and risk of complications, and urologic and nerve-sparing procedures may be required in such cases. Preoperative evaluation of such scenarios will allow proper counseling of patients and facilitate adequate surgical planning in referral centers; moreover, when necessary, it can guide the constitution of a dedicated multidisciplinary surgical team as an alternative to treatment by a pelvic surgeon alone. Detailed imaging evaluation of DIE lesions and their extension is crucial for clinical management of affected patients. It can facilitate optimization of surgical timing and strategies, thereby potentially preventing ineffective, or even harmful, repeated procedures.

Reliability of cephalometric landmark identification on three-dimensional computed tomographic images.

Our aims were to evaluate the reliability of three-dimensional (3D) cephalometric landmark identification in 3D images, and to propose an improved protocol for determining these landmarks. Computed tomographic (CT) images of 13 landmarks were obtained. One that did not show any artifacts, asymmetry in maxillofacial structures, or bony defects, was selected. Two orthodontic practitioners identified 3D cephalometric landmarks 10 times at one-week intervals. The distances of 26 landmarks were measured on the basis of three reference planes (coronal, horizontal, and sagittal). Ten mean (SD) measurements from each examiner were calculated, and the maximum and minimum values and the difference from the 10 measurements of each one were measured at a 95% confidence interval. Interexaminer differences for the three planes were found in the upper right first molar, point A, both gonions, left orbitale, and both porions. The lower right first molar, foramen magnum, gnathion, nasion, and pogonion showed interexaminer differences in two planes. Menton, basion, posterior nasal spine, upper and lower left first molar, and right mental foramen showed interexaminer differences in only one plane. With reference to intraexaminer differences, poor repeatability was observed for gonion, orbitale, condylion, and porion. Reliable 3D landmarks are the meeting point of sutures, distinct structures at converging planes, landmarks positioned in the midline, distinct anatomical structures such as the mental foramen, and teeth using multiplanar views.

Automatic localization of cephalometric landmarks based on convolutional neural network.

INTRODUCTION: Cephalometry plays an important role in the diagnosis and treatment of orthodontics and orthognathic surgery. This study intends to develop an automatic landmark location system to make cephalometry more convenient. METHODS: In this study, 512 lateral cephalograms were collected, and 37 landmarks were included. The coordinates of all landmarks in the 512 films were obtained to establish a labeled dataset: 312 were used as a training set, 100 as a validation set, and 100 as a testing set. An automatic landmark location system based on the convolutional neural network was developed. This system consisted of a global detection module and a locally modified module. The lateral cephalogram was first fed into the global module to obtain an initial estimate of the landmark's position, which was then adjusted with the locally modified module to improve accuracy. Mean radial error (MRE) and success detection rate (SDR) within the range of 1-4 mm were used to evaluate the method. RESULTS: The MRE of our validation set was 1.127 ± 1.028 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 45.95%, 89.19%, 97.30%, 97.30%, and 97.30%. The MRE of our testing set was 1.038 ± 0.893 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 54.05%, 91.89%, 97.30%, 100%, 100%, and 100%. CONCLUSIONS: In this study, we proposed a new automatic landmark location system on the basis of the convolutional neural network. The system could detect 37 landmarks with high accuracy. All landmarks are commonly used in clinical practice and could meet the requirements of different cephalometric analysis methods.

Refining Orthognathic Surgery Results by Synchronous Cheek Fat Compartment Augmentation with Fat Grafting in Adult Females with Class III Skeletal Profiles.

SUMMARY: Patients with maxillomandibular disharmony may present with a flat to concave midface. The effects of orthognathic surgery concomitant with midface fat grafting on facial appearance and midface volumetric and positional change have not formally been assessed to date. The authors' approach for synchronous orthognathic surgery and fat grafting is described and evaluated. Adult female patients (n = 20) who underwent synchronous two-jaw orthognathic surgery and cheek-specific fat grafting (1.9 ± 0.6 cm3 per side) for correction of skeletal class III deformity and anteromedial cheek deficiency were prospectively included. Preoperative and postoperative photographs were appraised by 42 blinded raters using facial appearance scales for beauty, attractiveness, and pleasantness parameters. The three-dimensional midface soft-tissue volume change and postoperative cheek mass position were computed. Facial imaging data from gender-, ethnic-, and facial pattern-matched adult patients (n = 20) who underwent isolated two-jaw orthognathic surgery (n = 20) were included for comparison. The three-dimensional facial norms database-derived cheek mass position information (2.19 ± 1.31mm) was also adopted for analysis. Patients treated with the synchronous procedure had significantly (p < 0.001) increased facial appearance-related perception change for beauty (2.9 ± 1.6), attractiveness (2.8 ± 1.8), and pleasantness (3.0 ± 1.5) parameters, three-dimensional midface volume change (1.8 ± 0.5 cm3), and postoperative cheek mass position (2.16 ± 0.47 mm) in comparison with those treated with the isolated procedure (2.0 ± 1.5, 1.9 ± 1.6, 2.3 ± 1.6, 0.6 ± 0.2 cm3, and 1.84 ± 0.43 mm, respectively). Healthy female individuals had similar and larger cheek mass position than patients treated with synchronous (p > 0.05) and isolated (p < 0.001) procedures, respectively. Synchronous orthognathic surgery and check-specific fat grafting resulted in superior enhancement of facial appearance and midface volume and position compared with isolated orthognathic surgery. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.

Automatic landmark annotation in 3D surface scans of skulls: Methodological proposal and reliability study.

BACKGROUND AND OBJECTIVES: Craniometric landmarks are essential in many biomedical applications, such as morphometric analysis or forensic identification. The process of locating landmarks is usually a manual and slow task, highly influenced by fatigue, skills and the experience of the practitioner. Localization errors are propagated and magnified in subsequent steps, which can result in incorrect measurements or assumptions. Thereby, standardization, reliability and reproducibility lay the foundations for the necessary accuracy in subsequent measurements or anatomical analysis. In this paper, we present an automatic method to annotate 3D surface skull models taking into account anatomical and geometrical features. METHODS: The proposed method follows a hybrid structure where a deformable template is used to initialize the landmark positions. Then, a refinement stage is applied using prior anatomical knowledge to ensure a correct placement. Our proposal is validated over thirty 3D skull scans of male Caucasians, acquired by hand-held surface scanning, and a set of 58 craniometric landmarks. A statistical analysis was carried out to analyze the inter- and intra-observer variability of manual annotations and the automatic results, along with a visual assessment of the final results. RESULTS: Inter-observer errors show significant differences, which are reflected in the expert consensus used as reference. The average localization error was 2.19±1.5 mm when comparing the automatic landmarks to the reference location. The subsequent visual analysis confirmed the reliability of the refinement method for most landmarks. CONCLUSIONS: Repeated manual annotations show a high variability depending on both skills and expertise of the observer, and landmarks' location and characteristics. In contrast, the automatic method provides an accurate, robust and reproducible alternative to the tedious and error-prone task of manual landmarking.

Anthropometric measurements of patellar ridge using computed tomography-based three-dimensional computer models.

BACKGROUND: The objectives of this study were to investigate the anatomic morphology of patellar ridge using computed tomography-based three-dimensional (3D) computer models and to assess the center of the patellar ridge after virtual resections. METHODS: We selected 80 patients, 40 males (age, 33.2±6.8 years) and 40 females (age, 30.6±7.2 years), who were slightly symptomatic with soft tissue injury of the knee joint. The right or left knees were scanned by computed tomography (CT). The CT data of 160 knees was used to construct 3D computer models by image analysis software (Mimics). Variables such as the angle between the patellar ridge and patellar long axis, the distance between the center of the patellar ridge and the center of patellar cut after virtual resections were measured. We detect differences between the sides and genders with the 3D computer models by Student's t test. Simple linear regression and correlation test was used to correlate the patellar ridge center to the center of the patellar cut. RESULTS: According to the available data, there were significant gender differences in the length and width of patellar cut after virtual resections even with strict control for the height and weight of the patients. The angle between the patellar ridge and the patellar long axis was 11.24° ± 3.62°. The angle in male patients was 10.17° ± 4.82°, and it was 12.28°± 3.78° in female patients. The morphological difference was statistically significant (P < 0.05). After using the subchondral method to virtually resect the patellae, with reference to the center of the patellar cut, the center of the patellar ridge lies superiorly and medially in 88.75%, inferiorly and medially in 8.75%, laterally and superiorly in 2.5%, and in no case laterally and inferiorly. The intra-observer reliability regarding the dimensional measurements was excellent in this study. CONCLUSIONS: Advances in 3D computer models had resulted in the availability of preoperative measurement and virtual planning. The anthropometric dimensions of this study could provide general information for guiding surgical management of the patella in total knee arthroplasty (TKA) and were useful in designing patellar implants. CLINICAL RELEVANCE: The placement of the patellar component during TKA differs from one patella to another. The anatomic morphology information of the patellar ridge is helpful for surgeons to perform patellar resurfacing in TKA.

The External Obturator Footprint Is a Usable, Accurate, and Reliable Landmark for Stem Depth in Direct Anterior THA.

BACKGROUND: Previous CT and cadaver studies have suggested that the external obturator footprint might be used as a landmark for stem depth in direct anterior THA. Instructions on where to template this structure with small variability in height have been developed but have not been tested in daily clinical practice. QUESTIONS/PURPOSES: In this study we sought to investigate the (1) usability, (2) accuracy, and (3) reliability of the external obturator footprint as a landmark for stem depth in direct anterior THA. METHODS: The distance between the superior border of the external obturator tendon and the shoulder of the stem was measured intraoperatively in all patients (n = 135) who underwent primary THA via a direct anterior approach performed by the senior author between November 2019 and October 2020. The landmark was considered useful when two of thre`e evaluators agreed that the intersection of the vertical line comprised of the lateral wall of the trochanteric fossa and the oblique line formed by the intertrochanteric crest was clearly visible on the preoperative planning radiograph, and when the landmark was furthermore identified with certainty during surgery. Accuracy was defined as the degree of agreement (categorical for thresholds of 2 and 5 mm, the latter representing the threshold for developing unphysiological gait parameters) between the intraoperative distance and radiographic distance as measured on intraoperative fluoroscopy images or postoperative radiographs, which were calibrated based on femoral head sizes in a software program commonly used for templating. Intrarater reliability was defined as the degree of agreement (categorical for thresholds of 1 mm, which we considered an acceptable measurement error) between the ratings of one observer, who measured the radiographic distance on two different occasions separated by a washout period of at least 2 weeks. Interrater reliability was defined as the degree of agreement (categorical for thresholds of 1 mm, which we considered an acceptable measurement error) between the ratings of three observers with varying levels of experience (a fellowship-trained hip surgeon, a hip surgery fellow, and a medical student). RESULTS: The landmark was considered useful in 77% (104 of 135) of patients who underwent direct anterior THA based on the observations that the trochanteric fossa was clearly visible on the planning radiograph in 117 patients and that the tendon was identified with certainty during surgery in 118 patients. There was good-to-excellent accuracy (intraclass correlation coefficient 0.75-087), and intrarater reliability (ICC 0.99) and interrater reliability (ICC 0.99) were both excellent. CONCLUSION: This clinical study showed that the external obturator footprint is a useful, accurate, and reliable landmark for stem depth in direct anterior THA. CLINICAL RELEVANCE: The external obturator landmark allows the surgeon to position the stem within a range of the templated depth that is beneath the threshold for the development of unphysiological gait parameters. Although strictly speaking it was found useful in 77% of patients in this study, we found that this percentage of usability can easily be improved to around 90% by providing the radiology lab technician with instructions to correct external rotation of the foot during the taking of the planning radiograph. Future studies could compare the established (in)equality in leg length in patients using the external obturator landmark with computer-assisted surgery.

Endoscopic evaluation of surgically altered bowel in inflammatory bowel disease: a consensus guideline from the Global Interventional Inflammatory Bowel Disease Group.

The majority of patients with Crohn's disease and a proportion of patients with ulcerative colitis will ultimately require surgical treatment despite advances in diagnosis, therapy, and endoscopic interventions. The surgical procedures that are most commonly done include bowel resection with anastomosis, strictureplasty, faecal diversion, and ileal pouch. These surgical treatment modalities result in substantial alterations in bowel anatomy. In patients with inflammatory bowel disease, endoscopy plays a key role in the assessment of disease activity, disease recurrence, treatment response, dysplasia surveillance, and delivery of endoscopic therapy. Endoscopic evaluation and management of surgically altered bowel can be challenging. This consensus guideline delineates anatomical landmarks and endoscopic assessment of these landmarks in diseased and surgically altered bowel.