Scale-based landmark for internal organs assessments of common carp (Cyprinus carpio) during digital radiography and ultrasonography imaging.

Medical imaging techniques such as digital radiography and ultrasonography are non-invasive and provide precise results for examining internal organs and structures within fish. Their effectiveness can be further enhanced by using body parts like scales as markers for the organs beneath them. This study utilized the number of scales as landmarks in digital radiography and ultrasonography to non-invasively evaluate the muscles, bones, and images of internal and reproductive organs of common carp (Cyprinus carpio). Digital radiography was performed in the dorsoventral and lateral views of the fish, whereas ultrasonography was conducted in longitudinal and transverse views on sequence scale numbers with brightness and colour Doppler-modes. Digital radiography of the common carp revealed the whole-body morphology, including the bony parts from the head, pectoral fins, dorsal fins, pelvic fins, anal fins, and vertebrae to the tail that appeared radiopaque. Internal organs were also observed, with the swim bladder and heart appeared radiolucent, while the intestines, liver, testes, and ovaries appeared radiopaque. Ultrasonography in brightness mode displayed the digestive organs, reproductive organs, and muscle thickness. Additionally, colour Doppler mode demonstrated blood flow within the heart's ventricle.

Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.

Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT scans. However, these approaches have relied on uniform datasets from a single center or imaging device but without considering patient ethnicity. In addition, previous works have considered a limited number of clinically relevant cephalometric landmarks and the approaches were computationally infeasible, both impairing integration into clinical workflow. Here our aim is to analyze the clinical applicability of a light-weight deep learning neural network for fast localization of 46 clinically significant cephalometric landmarks with multi-center, multi-ethnic, and multi-device data consisting of 309 CBCT scans from Finnish and Thai patients. The localization performance of our approach resulted in the mean distance of 1.99 ± 1.55 mm for the Finnish cohort and 1.96 ± 1.25 mm for the Thai cohort. This performance turned out to be clinically significant i.e., ≤ 2 mm with 61.7% and 64.3% of the landmarks with Finnish and Thai cohorts, respectively. Furthermore, the estimated landmarks were used to measure cephalometric characteristics successfully i.e., with ≤ 2 mm or ≤ 2° error, on 85.9% of the Finnish and 74.4% of the Thai cases. Between the two patient cohorts, 33 of the landmarks and all cephalometric characteristics had no statistically significant difference (p < 0.05) measured by the Mann-Whitney U test with Benjamini-Hochberg correction. Moreover, our method is found to be computationally light, i.e., providing the predictions with the mean duration of 0.77 s and 2.27 s with single machine GPU and CPU computing, respectively. Our findings advocate for the inclusion of this method into clinical settings based on its technical feasibility and robustness across varied clinical datasets.

Automatic quantification of scapular and glenoid morphology from CT scans using deep learning.

OBJECTIVES: To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis. MATERIALS AND METHODS: First, we used deep learning to segment the scapula from CT images and then to identify the location of 13 landmarks on the scapula, 9 of them to establish a coordinate system unaffected by osteoarthritis-related changes, and the remaining 4 landmarks on the glenoid cavity to determine the glenoid size and orientation in this scapular coordinate system. The glenoid version, glenoid inclination, critical shoulder angle, glenopolar angle, glenoid height, and glenoid width were subsequently measured in this coordinate system. A 5-fold cross-validation was performed to evaluate the performance of this approach on 60 normal/non-osteoarthritic and 56 pathological/osteoarthritic scapulae. RESULTS: The Dice similarity coefficient between manual and automatic scapular segmentations exceeded 0.97 in both normal and pathological cases. The average error in automatic scapular and glenoid landmark positioning ranged between 1 and 2.5 mm and was comparable between the automatic method and human raters. The automatic method provided acceptable estimates of glenoid version (R2 = 0.95), glenoid inclination (R2 = 0.93), critical shoulder angle (R2 = 0.95), glenopolar angle (R2 = 0.90), glenoid height (R2 = 0.88) and width (R2 = 0.94). However, a significant difference was found for glenoid inclination between manual and automatic measurements (p < 0.001). CONCLUSIONS: This open-source deep learning model enables the automatic quantification of scapular and glenoid morphology from CT scans of patients with glenohumeral osteoarthritis, with sufficient accuracy for clinical use.

Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.

OBJECTIVES: This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. SEARCH METHODS: An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. SELECTION CRITERIA: Studies that employed AI for 2D or 3D cephalometric landmark detection were included. DATA COLLECTION AND ANALYSIS: The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. RESULTS: Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2 mm (1.39 mm; 95% confidence interval: 0.85-1.92 mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8 mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1 min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). CONCLUSION: The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. REGISTRATION: PROSPERO: CRD42022328800.

Automatic cephalometric landmark identification with artificial intelligence: An umbrella review of systematic reviews.

OBJECTIVES: The transition from manual to automatic cephalometric landmark identification has not yet reached a consensus for clinical application in orthodontic diagnosis. The present umbrella review aimed to assess artificial intelligence (AI) performance in automatic 2D and 3D cephalometric landmark identification. DATA: A combination of free text words and MeSH keywords pooled by boolean operators: Automa* AND cephalo* AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "learning"). SOURCES: A search strategy without a timeframe setting was conducted on PubMed, Scopus, Web of Science, Cochrane Library and LILACS. STUDY SELECTION: The study protocol followed the PRISMA guidelines and the PICO question was formulated according to the aim of the article. The database search led to the selection of 15 articles that were assessed for eligibility in full-text. Finally, 11 systematic reviews met the inclusion criteria and were analyzed according to the risk of bias in systematic reviews (ROBIS) tool. CONCLUSIONS: AI was not able to identify the various cephalometric landmarks with the same accuracy. Since most of the included studies' conclusions were based on a wrong 2 mm cut-off difference between the AI automatic landmark location and that allocated by human operators, future research should focus on refining the most powerful architectures to improve the clinical relevance of AI-driven automatic cephalometric analysis. CLINICAL SIGNIFICANCE: Despite a progressively improved performance, AI has exceeded the recommended magnitude of error for most cephalometric landmarks. Moreover, AI automatic landmarking on 3D CBCT appeared to be less accurate compared to that on 2D X-rays. To date, AI-driven cephalometric landmarking still requires the final supervision of an experienced orthodontist.

A novel approach to craniofacial analysis using automated 3D landmarking of the skull.

Automatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising of 6707 quasi-landmarks, which can aid in the classification and quantification of variation over human facial bone surfaces. Automatic craniofacial bone phenotyping was conducted on a dataset of 31 skull scans obtained through cone-beam computed tomography (CBCT) imaging. The MeshMonk framework facilitated the non-rigid alignment of the constructed craniofacial bone mask with each individual target mesh. To gauge the accuracy and reliability of this automated process, 20 anatomical facial landmarks were manually placed three times by three independent observers on the same set of images. Intra- and inter-observer error assessments were performed using root mean square (RMS) distances, revealing consistently low scores. Subsequently, the corresponding automatic landmarks were computed and juxtaposed with the manually placed landmarks. The average Euclidean distance between these two landmark sets was 1.5 mm, while centroid sizes exhibited noteworthy similarity. Intraclass coefficients (ICC) demonstrated a high level of concordance (> 0.988), with automatic landmarking showing significantly lower errors and variation. These results underscore the utility of this newly developed single-surface craniofacial bone mask, in conjunction with the MeshMonk framework, as a highly accurate and reliable method for automated phenotyping of the facial region of human skulls from CBCT and CT imagery. This craniofacial template bone mask expansion of the MeshMonk toolbox not only enhances our capacity to study craniofacial bone variation but also holds significant potential for shedding light on the genetic, developmental, and evolutionary underpinnings of the overall human craniofacial structure.

Physeal Location on the Distal Femoral Cortical Surface Is Consistently Proximal to Apparent Location on the Perfect Lateral Radiograph: A 3-Dimensional Digitally Reconstructed Radiograph Study.

INTRODUCTION: The perfect knee lateral radiograph visualizes anatomic landmarks on the distal femur for clinical and scientific purposes. However, radiographic imaging is a two-dimensional (2D) representation of a three-dimensional (3D) physis. The aim of this study was to characterize the perceived radiographic projection of the femoral physis using perfect lateral digitally reconstructed radiographs (DRRs) and to evaluate discrepancies from this projection to the physis at the lateral and medial cortices. METHODS: Pediatric patients from a cohort of CT scans were analyzed. Inclusion criteria were an open physis; exclusion criteria were any implant or pathology affecting the physis. CT scans were imported into 3D imaging software and transformed into lateral DRRs and 3D renderings of the femur. The physis was divided into four equal segments, with fiducial markers placed at the "anterior," "midpoint," and "posterior" points. Lines extended from these points in the lateral and medial direction. The vertical distance from these lines, representing the radiographic projection of the physis, was measured relative to the physis at the lateral and medial cortex of the femur on coronal CT slices. RESULTS: Thirty-one patients were included. On the perfect lateral radiograph DRR, the physis on the medial cortex was located proximal to the visualized physis by 6.64 ± 1.74 mm, 11.95 ± 1.67 mm, and 14.30 ± 1.75 mm at the anterior (25%), midpoint (50%), and posterior (75%) locations, respectively. On the lateral side, the physis on the lateral cortex was proximal to the visualized physis by 2.19 ± 1.13 mm, 3.71 ± 1.19 mm, and 6.74 ± 1.25 mm at the anterior, midpoint, and posterior locations, respectively. DISCUSSION: In this cohort of pediatric patients, the location of the cortical physis was, in all areas measured, proximal to the projection of the visualized physis as seen on the perfect knee lateral DRR. The distance from radiographic physis to cortical physis was greater at the medial cortex compared with the lateral cortex. STUDY DESIGN: Descriptive laboratory study. LEVEL OF EVIDENCE: III, observational radiographic anatomic study.

Involution Transformer Based U-Net for Landmark Detection in Ultrasound Images for Diagnosis of Infantile DDH.

The B-mode ultrasound based computer-aided diagnosis (CAD) has demonstrated its effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants, which can conduct the Graf's method by detecting landmarks in hip ultrasound images. However, it is still necessary to explore more valuable information around these landmarks to enhance feature representation for improving detection performance in the detection model. To this end, a novel Involution Transformer based U-Net (IT-UNet) network is proposed for hip landmark detection. The IT-UNet integrates the efficient involution operation into Transformer to develop an Involution Transformer module (ITM), which consists of an involution attention block and a squeeze-and-excitation involution block. The ITM can capture both the spatial-related information and long-range dependencies from hip ultrasound images to effectively improve feature representation. Moreover, an Involution Downsampling block (IDB) is developed to alleviate the issue of feature loss in the encoder modules, which combines involution and convolution for the purpose of downsampling. The experimental results on two DDH ultrasound datasets indicate that the proposed IT-UNet achieves the best landmark detection performance, indicating its potential applications.

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.

The anterior lobe of the parotid gland: a CT sialographic study.

OBJECTIVES: To investigate the imaging and anatomic features of the anterior lobe (AL) of the superficial parotid gland (SPG). METHODS: Computed tomographic sialography examinations were undertaken for 142 parotid glands in 77 patients. Whole computer tomography (CT) data were analyzed using multi-planar reformation and maximum intensity projection to generate sialographic CT images. The tributary ducts of the SPG were analyzed to classify the parotid morphology. Three-dimensional analyses were used to investigate the AL and its relationship with adjacent anatomic landmarks. RESULTS: Four major types (I-IV) and 2 minor types (V-VI) of the AL and the superficial parotid gland were observed. Type I AL (83/142) was contiguous and not separated from the retromandibular parotid gland. Type II AL (16/142) was detached from the retromandibular parotid gland with 1-4 tributary ducts. Type III AL (12/142) showed a small isolated lobe above the Stensen duct around the anterior edge of the masseter. Type IV (28/142) showed the absence of the AL. Type V (3/142) shows the absence of the retromandibular parotid gland. Type VI (3/142) showed the presence of ectopic salivary gland beneath the Stensen duct anterior to the retromandibular parotid gland. CONCLUSIONS: The AL gives rise to the morphological variations of the superficial parotid gland. AL also gives rise to the accessory parotid gland when it is detached from the retromandibular parotid gland.

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.

Landmark Localization From Medical Images With Generative Distribution Prior.

In medical image analysis, anatomical landmarks usually contain strong prior knowledge of their structural information. In this paper, we propose to promote medical landmark localization by modeling the underlying landmark distribution via normalizing flows. Specifically, we introduce the flow-based landmark distribution prior as a learnable objective function into a regression-based landmark localization framework. Moreover, we employ an integral operation to make the mapping from heatmaps to coordinates differentiable to further enhance heatmap-based localization with the learned distribution prior. Our proposed Normalizing Flow-based Distribution Prior (NFDP) employs a straightforward backbone and non-problem-tailored architecture (i.e., ResNet18), which delivers high-fidelity outputs across three X-ray-based landmark localization datasets. Remarkably, the proposed NFDP can do the job with minimal additional computational burden as the normalizing flows module is detached from the framework on inferencing. As compared to existing techniques, our proposed NFDP provides a superior balance between prediction accuracy and inference speed, making it a highly efficient and effective approach. The source code of this paper is available at https://github.com/jacksonhzx95/NFDP.

Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval.

OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs). MATERIALS AND METHODS: A total of 198 Japanese preadolescent children, who had skeletal Class I malocclusion and whose Lat-cephs were available at age 8 years (T0) and 10 years (T1), were allocated into the training, validation, and test phases (n = 161, n = 17, n = 20). Orthodontists and the CNN model identified 28 hard-tissue landmarks (HTL) and 19 soft-tissue landmarks (STL). The mean prediction error values were defined as 'excellent,' 'very good,' 'good,' 'acceptable,' and 'unsatisfactory' (criteria: 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm, respectively). The degree of accurate prediction percentage (APP) was defined as 'very high,' 'high,' 'medium,' and 'low' (criteria: 90%, 70%, and 50%, respectively) according to the percentage of subjects that showed the error range within 1.5 mm. RESULTS: All HTLs showed acceptable-to-excellent mean PE values, while the STLs Pog', Gn', and Me' showed unsatisfactory values, and the rest showed good-to-acceptable values. Regarding the degree of APP, HTLs Ba, ramus posterior, Pm, Pog, B-point, Me, and mandibular first molar root apex exhibited low APPs. The STLs labrale superius, lower embrasure, lower lip, point of lower profile, B', Pog,' Gn' and Me' also exhibited low APPs. The remainder of HTLs and STLs showed medium-to-very high APPs. CONCLUSION: Despite the possibility of using the CNN model to predict growth, further studies are needed to improve the prediction accuracy in HTLs and STLs of the chin area.

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.

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.

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.