AIMC Topic: Anatomic Landmarks

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Accuracy comparative study of automatic landmarking and diagnostic models on lateral cephalograms.

Progress in orthodontics
BACKGROUND: The application of deep learning techniques in cephalometric analysis has become increasingly prominent. Although automatic landmarking models for cephalometric analysis have been developed, their accuracy still requires validation and re...

Comparison of 2D, 2.5D, and 3D landmark localization networks for 3D cephalometry in CT images.

BMC oral health
BACKGROUND: Accurate landmark localization is important for three-dimensional (3D) cephalometric analysis. Although deep learning has shown promising performance for 3D landmark localization, the high computational burden of processing volumetric dat...

Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network.

BMC oral health
BACKGROUND: Estimating craniofacial patterns is essential for successful orthodontic treatment. However, conventional static measurements are inadequate for capturing dynamic changes, and manual cephalometric analysis is labor-intensive and requires ...

Populational influence on cephalometric landmark identification: performance of two AI-driven software programs in Brazilian and Korean images.

BMC oral health
OBJECTIVE: To assess the performance of cephalometric landmark identification performed by two AI-driven software programs in images from different populations (Brazilian and Korean).

Real-Time Identification of Cricothyrotomy Landmarks in Emergency Care and Obstetric Patients Using Wireless Handheld Ultrasound and Edge-Computing Artificial Intelligence: A Prospective Observational Study.

Journal of medical systems
This study aimed to develop machine learning-based algorithms to assist physicians in ultrasound-guided localization of the cricoid cartilage (CC), thyroid cartilage (TC), and cricothyroid membrane (CTM) for cricothyroidotomy. Adult female participan...

Evaluation of artificial intelligence-based cephalometric tracing versus semi-automatic and manual tracing.

BMC oral health
BACKGROUND: Artificial intelligence (AI)-based cephalometric tracing has emerged as a promising tool that reduces operator variability and offers standardized, rapid, and reproducible assessments. This study aimed to evaluate the reliability and accu...

Multimodal deep learning for cephalometric landmark detection and treatment prediction.

Scientific reports
In orthodontics and maxillofacial surgery, accurate cephalometric analysis and treatment outcome prediction are critical for clinical decision-making. Traditional approaches rely on manual landmark identification, which is time-consuming and subject ...

Cephalometric landmark detection using vision transformers with direct coordinate prediction.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Cephalometric Landmark Detection (CLD), i.e. annotating interest points in lateral X-ray images, is the crucial first step of every orthodontic therapy. While CLD has immense potential for automation using Deep Learning methods, carefully crafted con...

Landmark display system for laparoscopic inguinal hernia repair using artificial intelligence.

Surgical endoscopy
BACKGROUND: Chronic postoperative inguinal pain (CPIP) is a major complication of inguinal hernia repair and significantly affects patients' quality of life. Despite the widespread use of transabdominal preperitoneal repair (TAPP), CPIP still occurs....

Automated landmark-based mid-sagittal plane: reliability for 3-dimensional mandibular asymmetry assessment on head CT scans.

Clinical oral investigations
OBJECTIVE: The determination of the mid-sagittal plane (MSP) on three-dimensional (3D) head imaging is key to the assessment of facial asymmetry. The aim of this study was to evaluate the reliability of an automated landmark-based MSP to quantify man...