OBJECTIVE The analysis of sagittal alignment by measuring spinopelvic parameters has been widely adopted among spine surgeons globally, and sagittal imbalance is a well-documented cause of poor quality of life. These measurements are time-consuming but necessary to make, which creates a growing need for an automated analysis tool that measures spinopelvic parameters with speed, precision, and reproducibility without relying on user input. This study introduces and evaluates an algorithm based on artificial intelligence (AI) that fully automatically measures spinopelvic parameters. METHODS Two hundred lateral lumbar radiographs (pre- and postoperative images from 100 patients undergoing lumbar fusion) were retrospectively analyzed by board-certified spine surgeons who digitally measured lumbar lordosis, pelvic incidence, pelvic tilt, and sacral slope. The novel AI algorithm was also used to measure the same parameters. To evaluate the agreement between human and AI-automated measurements, the mean error (95% CI, SD) was calculated and interrater reliability was assessed using the 2-way random single-measure intraclass correlation coefficient (ICC). ICC values larger than 0.75 were considered excellent. RESULTS The AI algorithm determined all parameters in 98% of preoperative and in 95% of postoperative images with excellent ICC values (preoperative range 0.85–0.92, postoperative range 0.81–0.87). The mean errors were smallest for pelvic incidence both pre- and postoperatively (preoperatively −0.5° [95% CI −1.5° to 0.6°] and postoperatively 0.0° [95% CI −1.1° to 1.2°]) and largest preoperatively for sacral slope (−2.2° [95% CI −3.0° to −1.5°]) and postoperatively for lumbar lordosis (3.8° [95% CI 2.5° to 5.0°]). CONCLUSIONS Advancements in AI translate to the arena of medical imaging analysis. This method of measuring spinopelvic parameters on spine radiographs has excellent reliability comparable to expert human raters. This application allows users to accurately obtain critical spinopelvic measurements automatically, which can be applied to clinical practice. This solution can assist physicians by saving time in routine work and by avoiding error-prone manual measurements.

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The precise and accurate measurement of implant wear, acetabular cup anteversion and inclination from routine anterior‐posterior radiographs still poses a challenge. Current approaches suffer from time‐consuming procedures accompanied by low and observer‐dependent accuracy and precision. We present and validate a novel, automated method for determining total hip arthroplasty parameters by comparing its accuracy and precision with methods in contemporary scientific literature. The algorithm uses CAD‐model‐based two dimensional‐three dimensional (2D‐3D)‐registration supported by convolutional neural networks. Two in‐vitro experimental set‐ups were designed to validate the proposed 2D‐3D‐method. The set‐ups provided 84 predefined wear values and 24 configurations of anteversion and inclination in 114 radiographs. Accuracy and precision were evaluated by systematically comparing the predefined ground truth and the automatically calculated values from in‐vitro X‐rays. In addition, an algorithm was developed and validated against physician's measurements on clinical X‐rays to determine the inclination of the interteardrop (ITL) and biischial line (BL) to account for the individual patient's pelvic rotation in the frontal plane. Using X‐rays from experimental set‐ups, the determined mean error was 0.014 mm (standard deviation: 0.020 mm; root‐mean‐square‐error: 0.024 mm) for wear in pelvic position, −0.01° (0.24°; 0.23°) for radiographic cup anteversion, and 0.11° (0.38°; 0.39°) for radiographic cup inclination. The inclination of ITL and BL was automatically determined in all clinical X‐rays with excellent interclass correlation coefficients of 0.95 and 0.91, respectively. The presented algorithm allows the accurate and precise evaluation of total hip arthroplasty parameters without additional equipment. The method might help to investigate different implant designs, biomaterials, and surgical techniques with greater objectivity.

Study design Retrospective, mono-centric cohort research study. Objectives The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS). Methods An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics. Results The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°). Conclusions The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.