Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort.

PURPOSE: The determination of bone age (BA) based on the hand and wrist, using the 70-year-old Greulich and Pyle (G&P) atlas, remains a widely employed practice in various institutions today. However, a more recent approach utilizing artificial intelligence (AI) enables automated BA estimation based on the G&P atlas. Nevertheless, AI-based methods encounter limitations when dealing with images that deviate from the standard hand and wrist projections. Generally, the extent to which BA, as determined by the G&P atlas, corresponds to the chronological age (CA) of a contemporary German population remains a subject of continued discourse. This study aims to address two main objectives. Firstly, it seeks to investigate whether the G&P atlas, as applied by the AI software, is still relevant for healthy children in Germany today. Secondly, the study aims to assess the performance of the AI software in handling non-strict posterior-anterior (p. a.) projections of the hand and wrist. MATERIALS AND METHODS: The AI software retrospectively estimated the BA in children who had undergone radiographs of a single hand using posterior-anterior and oblique planes. The primary purpose was to rule out any osseous injuries. The prediction error of BA in relation to CA was calculated for each plane and between the two planes. RESULTS: A total of 1253 patients (aged 3 to 16 years, median age 10.8 years, 55.7 % male) were included in the study. The average error of BA in posterior-anterior projections compared to CA was 3.0 (±â€Š13.7) months for boys and 1.7 (±â€Š13.7) months for girls. Interestingly, the deviation from CA tended to be even slightly lower in oblique projections than in posterior-anterior projections. The mean error in the posterior-anterior projection plane was 2.5 (±â€Š13.7) months, while in the oblique plane it was 1.8 (±â€Š13.9) months (p = 0.01). CONCLUSION: The AI software for BA generally corresponds to the age of the contemporary German population under study, although there is a noticeable prediction error, particularly in younger children. Notably, the software demonstrates robust performance in oblique projections. KEY POINTS: · Bone age, as determined by artificial intelligence, aligns with the chronological age of the contemporary German cohort under study.. · As determined by artificial intelligence, bone age is remarkably robust, even when utilizing oblique X-ray projections..

Artificial intelligence in osteoarthritis: repair by knee joint distraction shows association of pain, radiographic and immunological outcomes.

OBJECTIVES: Knee joint distraction (KJD) has been associated with clinical and structural improvement and SF marker changes. The current objective was to analyse radiographic changes after KJD using an automatic artificial intelligence-based measurement method and relate these to clinical outcome and SF markers. METHODS: Twenty knee osteoarthritis patients were treated with KJD in regular care. Radiographs and WOMAC were collected before and ∼1 year post-treatment. SF was aspirated before, during and after treatment; biomarker levels were assessed by immunoassay. Radiographs were analysed to obtain compartmental minimum and standardized joint space width (JSW), Kellgren-Lawrence (KL) grades, compartmental joint space narrowing (JSN) scores, and osteophytosis and sclerosis scores. Results were analysed for the most affected compartment (MAC) and least affected compartment. Radiographic changes were analysed using the Wilcoxon signed rank test for categorical and paired t-test for continuous variables. Linear regression was used to calculate associations between changes in JSW, WOMAC pain and SF markers. RESULTS: Sixteen patients could be evaluated. JSW, KL and JSN improved in around half of the patients, significant only for MAC JSW (P < 0.05). MAC JSW change was positively associated with WOMAC pain change (P < 0.04). Greater monocyte chemoattractant protein 1 (MCP-1) and lower TGFß-1 increases were significantly associated with changes in MAC JSW (P < 0.05). MCP-1 changes were positively associated with WOMAC pain changes (P < 0.05). CONCLUSION: Automatic radiographic measurements show improved joint structure in most patients after KJD in regular care. MAC JSW increased significantly and was associated with SF biomarker level changes and even with improvements in pain as experienced by these patients.

Artificial intelligence-generated hip radiological measurements are fast and adequate for reliable assessment of hip dysplasia : an external validation study.

AIMS: Hip dysplasia (HD) leads to premature osteoarthritis. Timely detection and correction of HD has been shown to improve pain, functional status, and hip longevity. Several time-consuming radiological measurements are currently used to confirm HD. An artificial intelligence (AI) software named HIPPO automatically locates anatomical landmarks on anteroposterior pelvis radiographs and performs the needed measurements. The primary aim of this study was to assess the reliability of this tool as compared to multi-reader evaluation in clinically proven cases of adult HD. The secondary aims were to assess the time savings achieved and evaluate inter-reader assessment. METHODS: A consecutive preoperative sample of 130 HD patients (256 hips) was used. This cohort included 82.3% females (n = 107) and 17.7% males (n = 23) with median patient age of 28.6 years (interquartile range (IQR) 22.5 to 37.2). Three trained readers' measurements were compared to AI outputs of lateral centre-edge angle (LCEA), caput-collum-diaphyseal (CCD) angle, pelvic obliquity, Tönnis angle, Sharp's angle, and femoral head coverage. Intraclass correlation coefficients (ICC) and Bland-Altman analyses were obtained. RESULTS: Among 256 hips with AI outputs, all six hip AI measurements were successfully obtained. The AI-reader correlations were generally good (ICC 0.60 to 0.74) to excellent (ICC > 0.75). There was lower agreement for CCD angle measurement. Most widely used measurements for HD diagnosis (LCEA and Tönnis angle) demonstrated good to excellent inter-method reliability (ICC 0.71 to 0.86 and 0.82 to 0.90, respectively). The median reading time for the three readers and AI was 212 (IQR 197 to 230), 131 (IQR 126 to 147), 734 (IQR 690 to 786), and 41 (IQR 38 to 44) seconds, respectively. CONCLUSION: This study showed that AI-based software demonstrated reliable radiological assessment of patients with HD with significant interpretation-related time savings.Cite this article: Bone Jt Open 2022;3(11):877-884.

Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study.

OBJECTIVES: Accurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance of a commercial available AI software by comparing its outputs with manually performed measurements. MATERIALS AND METHODS: The AI was trained on over 15,000 radiographs to measure various clinical angles and lengths from LLRs. We performed a retrospective single-center analysis on 295 LLRs obtained between 2015 and 2020 from male and female patients over 18 years. AI and expert measurements were performed independently. Kellgren-Lawrence score and reading time were assessed. All measurements were compared and non-inferiority, mean-absolute-deviation (sMAD), and intra-class-correlation (ICC) were calculated. RESULTS: A total of 295 LLRs from 284 patients (mean age, 65 years (18; 90); 97 (34.2%) men) were analyzed. The AI model produces outputs on 98.0% of the LLRs. Manually annotations were considered as 100% accurate. For each measurement, its divergence was calculated, resulting in an overall accuracy of 89.2% when comparing the AI outputs to the manually measured. AI vs. mean observer revealed an sMAD between 0.39 and 2.19° for angles and 1.45-5.00 mm for lengths. AI showed good reliability in all lengths and angles (ICC ≥ 0.87). Non-inferiority comparing AI to the mean observer revealed an equivalence-index (γ) between 0.54 and 3.03° for angles and - 0.70-1.95 mm for lengths. On average, AI was 130 s faster than clinicians. CONCLUSION: Automated measurements of knee alignment and length measurements produced with an AI tool result in reproducible, accurate measures with a time savings compared to manually acquired measurements.

Inter-observer agreement of vertebral fracture assessment with dual-energy x-ray absorptiometry equipment.

PURPOSE: To investigate the time and effort needed to perform vertebral morphometry, as well as inter-observer agreement for identification of vertebral fractures on vertebral fracture assessment (VFA) images. METHODS: Ninety-six images were retrospectively selected, and three radiographers independently performed semi-automatic 6-point morphometry. Fractures were identified and graded using the Genant classification. Time needed to annotate each image was recorded, and reader fatigue was assessed using a modified Simulator Sickness Questionnaire (SSQ). Inter-observer agreement was assessed per-patient and per-vertebra for detecting fractures of all grades (grades 1-3) and for grade 2 and 3 fractures using the kappa statistic. Variability in measured vertebral height was evaluated using the intraclass correlation coefficient (ICC). RESULTS: Per-patient agreement was 0.59 for grades 1-3 fracture detection, and 0.65 for grades 2-3 only. Agreement for per-vertebra fracture classification was 0.92. Vertebral height measurements had an ICC of 0.96. Time needed to annotate VFA images ranged between 91 and 540 s, with a mean annotation time of 259 s. Mean SSQ scores were significantly lower at the start of a reading session (1.29; 95% CI: 0.81-1.77) compared to the end of a session (3.25; 95% CI: 2.60-3.90; p < 0.001). CONCLUSION: Agreement for detection of patients with vertebral fractures was only moderate, and vertebral morphometry requires substantial time investment. This indicates that there is a potential benefit for automating VFA, both in improving inter-observer agreement and in decreasing reading time and burden on readers.

Biomechanical comparison of knotless vs. knotted suture anchors in the acetabular rim with respect to bone density.

BACKGROUND: Acetabular labral tears are managed with suture anchors providing good clinical outcomes. Knotless anchors are easier to use and have a quicker insertion time compared to knotted anchors. The purpose of this study was to compare the biomechanical behavior of two different anchor designs (knotted vs. knotless) in ultimate load testing in correlation with bone density in the acetabular rim. METHODS: Eighteen knotted Bio-FASTak and seventeen knotless PushLock anchors (both: Arthrex Inc., Naples, FL, USA) were inserted in the rims of two human acetabula, with known bone density distribution. The anchors were subjected to load-to-failure tests. Anchors were additionally tested in solid polyurethane foam with defined densities. FINDINGS: The Bio-FASTak group showed higher survival rates at 1, 2, and 3 mm displacement and was able to withstand significantly higher loads at 3 mm displacement (p = 0.031). There was no statistically significant difference in stiffness (p = 0.087), yield- (p = 0.190), and ultimate load (p = 0.222) between the two groups. Only the PushLock group showed correlation between bone volume over total volume (BV/TV) and stiffness (R = 0.750, p = 0.086) and between BV/TV and yield load (R = 0.838, p = 0.037). Experiments on solid polyurethane foam confirmed the correlation between the mechanical properties and tissue density for the same anchor. INTERPRETATION: PushLock shows similar biomechanical properties to the Bio-FASTak, but eliminates knot tying and potentially abrasive knots. In addition, biomechanical properties of the PushLock are governed by local bone density.

Impact of intensity discretization on textural indices of [18F]FDG-PET tumour heterogeneity in lung cancer patients.

Quantifying tumour heterogeneity from [18F]FDG-PET images promises benefits for treatment selection of cancer patients. Here, the calculation of texture parameters mandates an initial discretization step (binning) to reduce the number of intensity levels. Typically, three types of discrimination methods are used: lesion relative resampling (LRR) with fixed bin number, lesion absolute resampling (LAR) and absolute resampling (AR) with fixed bin widths. We investigated the effects of varying bin widths or bin number using 27 commonly cited local and regional texture indices (TIs) applied on lung tumour volumes. The data set were extracted from 58 lung cancer patients, with three different and robust tumour segmentation methods. In our cohort, the variations of the mean value as the function of the bin widths were similar for TIs calculated with LAR and AR quantification. The TI histograms calculated by LRR method showed distinct behaviour and its numerical values substantially effected by the selected bin number. The correlations of the AR and LAR based TIs demonstrated no principal differences between these methods. However, no correlation was found for the interrelationship between the TIs calculated by LRR and LAR (or AR) discretization method. Visual classification of the texture was also performed for each lesion. This classification analysis revealed that the parameters show statistically significant correlation with the visual score, if LAR or AR discretization method is considered, in contrast to LRR. Moreover, all the resulted tendencies were similar regardless the segmentation methods and the type of textural features involved in this work.

Reproducibility of MRI Dixon-Based Attenuation Correction in Combined PET/MR with Applications for Lean Body Mass Estimation.

UNLABELLED: The aim of this study was to assess the reproducibility of standard, Dixon-based attenuation correction (MR-AC) in PET/MR imaging. A further aim was to estimate a patient-specific lean body mass (LBM) from these MR-AC data. METHODS: Ten subjects were positioned in a fully integrated PET/MR system, and 3 consecutive multibed acquisitions of the standard MR-AC image data were acquired. For each subject and MR-AC map, the following compartmental volumes were calculated: total body, soft tissue (ST), fat, lung, and intermediate tissue (IT). Intrasubject differences in the total body and subcompartmental volumes (ST, fat, lung, and IT) were assessed by means of coefficients of variation (CVs) calculated across the 3 consecutive measurements and, again, across these measurements but excluding those affected by major artifacts. All subjects underwent a body composition measurement using air displacement plethysmography (ADP) that was used to calculate a reference LBMADP A second LBM estimate was derived from available MR-AC data using a formula incorporating the respective tissue volumes and densities as well as the subject-specific body weights. A third LBM estimate was obtained from a sex-specific formula (LBMFormula). Pearson correlation was calculated for LBMADP, LBMMR-AC, and LBMFormula Further, linear regression analysis was performed on LBMMR-AC and LBMADP. RESULTS: The mean CV for all 30 scans was 2.1 ± 1.9% (TB). When missing tissue artifacts were excluded, the CV was reduced to 0.3 ± 0.2%. The mean CVs for the subcompartments before and after exclusion of artifacts were 0.9 ± 1.1% and 0.7 ± 0.7% for the ST, 2.9 ± 4.1% and 1.3 ± 1.0% for fat, and 3.6 ± 3.9% and 1.3 ± 0.7% for the IT, respectively. Correlation was highest for LBMMR-AC and LBMADP (r = 0.99). Linear regression of data excluding artifacts resulted in a scaling factor of 1.06 for LBMMR-AC CONCLUSION: LBMMR-AC is shown to correlate well with standard LBM measurements and thus offers routine LBM-based SUV quantification in PET/MR. However, MR-AC images must be controlled for systematic artifacts, including missing tissue and tissue swaps. Efforts to minimize these artifacts could help improve the reproducibility of MR-AC.

High-resolution peripheral quantitative CT imaging: Cortical porosity, poor trabecular bone microarchitecture, and low bone strength in lung transplant recipients.

PURPOSE: To characterize bone microarchitecture and quantify bone strength in lung transplant (LT) recipients by using high-resolution (HR) peripheral quantitative computed tomographic (CT) imaging of the ultradistal radius. MATERIALS AND METHODS: After study approval by the local ethics committee, all participants provided written informed consent. Included were 118 participants (58 LT recipients [mean age, 46.8 years ± 1.9; 30 women, 28 men] and 60 control participants [mean age, 39.9 years ± 1.9; 41 women, 19 men]) between April 2010 and May 2012. HR peripheral quantitative CT of the ultradistal radius was performed and evaluated for bone mineral density and trabecular and cortical bone microarchitecture. Mechanical competence was quantified by microfinite element analysis. Differences between LT recipients and control participants were determined by using two-way factorial analysis of covariance with age adjustment. RESULTS: Total and trabecular bone mineral density were significantly lower (-13.4% and -16.4%, respectively; P = .001) in LT recipients than in healthy control participants. LT recipients had lower trabecular number (-9.7%; P = .004) and lower trabecular thickness (-8.1%; P = .025). Trabecular separation and trabecular network heterogeneity were higher (+24.3% and +63.9%, respectively; P = .007 and P = .012, respectively) in LT recipients. Moreover, there was pronounced cortical porosity (+31.3%; P = .035) and lower cortical thickness (-10.2%, P = .005) after LT. In addition, mechanical competence was impaired, which was reflected by low stiffness (-15.0%; P < .001), low failure force (-14.8%; P < .001), and low bone strength (-14.6%; P < .001). CONCLUSION: Men and women with recent LT showed severe deficits in cortical and trabecular bone microarchitecture. Poor bone microarchitecture and low bone strength are likely to contribute to high fracture susceptibility observed in LT recipients.

An intra-individual comparison of MRI, [18F]-FET and [18F]-FLT PET in patients with high-grade gliomas.

OBJECTIVES: Intra-individual spatial overlap analysis of tumor volumes assessed by MRI, the amino acid PET tracer [18F]-FET and the nucleoside PET tracer [18F]-FLT in high-grade gliomas (HGG). METHODS: MRI, [18F]-FET and [18F]-FLT PET data sets were retrospectively analyzed in 23 HGG patients. Morphologic tumor volumes on MRI (post-contrast T1 (cT1) and T2 images) were calculated using a semi-automatic image segmentation method. Metabolic tumor volumes for [18F]-FET and [18F]-FLT PETs were determined by image segmentation using a threshold-based volume of interest analysis. After co-registration with MRI the morphologic and metabolic tumor volumes were compared on an intra-individual basis in order to estimate spatial overlaps using the Spearman's rank correlation coefficient and the Mann-Whitney U test. RESULTS: [18F]-FLT uptake was negative in tumors with no or only moderate contrast enhancement on MRI, detecting only 21 of 23 (91%) HGG. In addition, [18F]-FLT uptake was mainly restricted to cT1 tumor areas on MRI and [18F]-FLT volumes strongly correlated with cT1 volumes (r = 0.841, p<0.001). In contrast, [18F]-FET PET detected 22 of 23 (96%) HGG. [18F]-FET uptake beyond areas of cT1 was found in 61% of cases and [18F]-FET volumes showed only a moderate correlation with cT1 volumes (r = 0.573, p<0.001). Metabolic tumor volumes beyond cT1 tumor areas were significantly larger for [18F]-FET compared to [18F]-FLT tracer uptake (8.3 vs. 2.7 cm3, p<0.001). CONCLUSION: In HGG [18F]-FET but not [18F]-FLT PET was able to detect metabolic active tumor tissue beyond contrast enhancing tumor on MRI. In contrast to [18F]-FET, blood-brain barrier breakdown seems to be a prerequisite for [18F]-FLT tracer uptake.

Ensemble based system for whole-slide prostate cancer probability mapping using color texture features.

We present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results. Ensembles of classifiers are built using expert-annotated tiles from training images, and scores for the probability of cancer presence are calculated from the responses of each classifier in the ensemble. Spatial filtering of tile-based texture features prior to classification results in increased heat-map coherence as well as AUC values of 95% using ensembles of either random forests or support vector machines. Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains.