Anteroposterior Radiographs Research Articles

Ellipsoidal patellar bone tunnel fixation with Toggleloc suspension system for medial patellofemoral ligament reconstruction: A 5 years follow-up.

This study aimed to evaluate the clinical and radiological features of the patella fixation technique using Toggleloc suspension system in a single ellipsoidal blind patellar tunnel during medial patellofemoral ligament (MPFL) reconstruction. This study included 52 patients (25 men, 27 women) who underwent MPFL reconstruction using a semitendinosus tendon graft. The graft was fixed to the ellipsoidal single blind tunnel opened on the medial side of the patella with an endobutton and was fixed to the femoral tunnel by using bioabsorbable screw. Clinical scores (Kujala score, Lysholm score, Tegner activity score and the visual analog scale [VAS] score) were evaluated preoperatively and at the end-follow up. Preoperative and postoperative radiological measurements (trochlea depth, sulcus angle, patellar height, patellar congruence angle, patellar tilt angle and lateral patellofemoral angle) were evaluated with X-ray (Merchant X-ray, anteroposterior and lateral radiography) and computed tomography (CT) of the knee. Postoperative patellar redislocation or subluxation was not observed in any patient. Patellar congruence angle, patellar tilt angle and lateral patellofemoral angle mean values were found to return to normal values in the postoperative period and the results were statistically significant. Also statistically significant improvement in all clinical scores postoperatively. According to the Insall-Salvati index (ISI) and Caton-Deschamps index (CDI) on lateral radiography of the knee at 30° flexion, patellar height decreased in the postoperative period statistically significant. The CDI was above 1.3 in 17 (%32) of our patients. Thirteen of these values decreased to normal values. No radiological progression of patellofemoral osteoarthritis was observed in all patients at the final follow-up evaluation. In cases of patellofemoral instability, fixation of the tendon graft in blind ellipsoid tunnel using the Toggleloc suspension system provides satisfactory patellar graft fixation strength, significant functional improvement and a low failure rate.

Femorotibial angle on short knee radiographs fails to accurately predict the lower limb mechanical alignment. A systematic review and meta-analysis on different femorotibial angle definitions and short knee radiograph types.

Hip-knee-ankle angle (HKA) on Full Limb Radiographs (FLRs) is the gold standard for coronal knee alignment assessment. Despite the widespread utilization of the more convenient femorotibial angle (FTA) on either antero-posterior (AP) or postero-anterior (PA) short knee radiographs (SKRs), its definition and correlation with HKA remains controversial. This review is the first to systematically investigate FTA-HKA correlation and the effect of different FTA methods and SKRs. Systematic literature search (Pubmed, Scopus, Cochrane Library) followed PRISMA guidelines, to evaluate studies examining the FTA-HKA correlation. Meta-analyses compared the 3 most common FTA methods, knee center determination method and SKR types. 17 studies (2597 patients, 3234 knees) were included. The strongest correlation with HKA (r = 0.78) was found for FTA Method 1 (angle formed by lines drawn from the midpoint of tibial spines to points 10 cm above and below the joint line). No significant differences were observed when grouping the FTA methods by knee center assessment (Group I, r = 0.78; Group II, r = 0.77). AP SKRs showed a trend towards stronger FTA-HKA correlation compared to PA SKRs, in both Method 1 (r = 0.79 vs 0.75) and Method 3 (r = 0.80 vs 0.66). Irrespective of its definition or type of SKR used, FTA lacks reliable accuracy in predicting the HKA in most knees. FLRs should be used whenever precise estimation of the patient's alignment is necessary. Caution is warranted in interpreting studies investigating knee alignment or knee arthroplasty outcomes based on FTA.

Prognostic power of criteria for symptomatic early knee osteoarthritis from a 2-year longitudinal observation of the Iwaki cohort study

PurposeThis study aimed to investigate whether symptomatic early knee osteoarthritis (EKOA) effectively predicts the incidence of definitive knee osteoarthritis (DKOA) in the general Japanese population. MethodsA total of 133 women from the Japanese Iwaki cohort study were enrolled and followed-up over 2 years in order to calculate the rate of progression of EKOA to DKOA. Weight-bearing anteroposterior bilateral knee radiographs and magnetic resonance imaging (MRI) were conducted at baseline and follow-up. Radiographs were classified according to the Kellgren–Lawrence grade. The presence of cartilage lesion, bone marrow lesions, attrition, cysts, osteophytes, and meniscal lesions was evaluated according to the Whole-Organ Magnetic Resonance Imaging Score (WORMS). Logistic regression analyses were performed to determine the predictive power of EKOA. ResultsOf 25 women with EKOA, 15 (60%) progressed to DKOA over 2 years, showing a relative risk (RR) of 1.44 compared with the non-osteoarthritis (non-OA) group (P ​= ​0.120). Logistic regression analysis showed associations of EKOA (P ​= ​0.048) and high body mass index (P ​< ​0.001) with progression to DKOA. Combined EKOA and meniscal lesions increased the RR for osteoarthritis incidence to 2.32 (P ​= ​0.004) compared with the non-OA group. Also, meniscus and bone marrow lesion scores on MRI of the EKOA group maintained high scores over 2 years. ConclusionsThe prognostic power of EKOA criteria was confirmed with a RR of 1.44. The combination of symptomatic EKOA criteria and MRI-detected meniscal lesions was a valuable predictor of progression to DKOA over 2 years in women without radiographic abnormalities.

Valgus stress knee radiographs accurately anticipate the bone resection in medial unicompartmental knee arthroplasty: Protocol validation using an image-based robotic system.

The objective of this study was to describe a planning method for medial unicompartmental knee arthroplasty (UKA) implantation using preoperative stress radiographs to measure the thickness of tibial and femoral bone resectionsand to validate this method with data from an image-based robotic surgery system. Having such method for preoperative planning would be of interest for surgeons performing UKA in order to anticipate optimal bone resection on both tibia and femoral sides. A new planning method for medial UKA based on valgus stress knee radiographs validated it with an image-based robotic surgery system (Restoris MCK, MAKO®, Stryker Corporation) was proposed. This retrospective study involved radiographic measurements of 76 patients who underwent image-based robotic medial UKA between April 2022 and February 2023. Preoperative anteroposterior stress radiographs of the knee were used to simulate UKA implantation. The UKA technique was based on Cartier's angle and aimed at restoring the joint line. The total dimension measured was 14 mm (8 mm for minimal tibial component and polyethylene insert + 4 mm for femoral component + 2 mm for safety laxity). Bone resections were measured in the preoperative valgus stress radiographs and then against the intraoperative bone resection data provided by the robotic system. Inter- and intra-observer reliability was assessed using 25 measurements. The mean planned tibial resection measured in the radiographs was 4.3 ± 0.4 [2.9-5.8], while the mean robotic resection was 4.2 ± 0.5 [2.7-5.8] (mean difference = 0.15 mm, 95% confidence interval [CI] [-0.27 to 0.57]). There was a strong correlation between these two values (Pearson's rank R = 0.79, p < 0.001). Intra- and inter-observer reliability were also very strong (Pearson's rank R = 0.91, p < 0.001, and Pearson's rank R = 0.82, p < 0.001, respectively). The mean planned femoral bone resection measured in the radiographs was 2.7 ± 0.7 mm [1-4.5], while the mean robotic resection was 2.5 ± 0.9 [1-5] (mean difference = 0.21 mm, 95% CI [-0.66 to 1.08]). There was a strong correlation between these two values (Pearson's rank R = 0.82, p < 0.001). Intra- and inter-observer reliability were also strong (Pearson's rank R = 0.88, p < 0.001, and Pearson's rank R = 0.84, p < 0.001, respectively). This study describes and validates with robotic information a simple and reproducible preoperative planning method to determine femoral and tibial bone resection for medial UKA implantation using antero-posterior valgus stress knee radiographs, leaving a medial safety laxity of 2 mm. It represents a very valuable contribution to the understanding of UKA principles, which can serve to extend its indications and increase reproducibility of the surgical technique. III. Retrospective cohort study.

Comprehensive Approach of the Diagnosis, Treatment, and Medical Rehabilitation of Patients with Spondylolisthesis

: Spondylolisthesis represents the slippage of one vertebra over another, and is widely diagnosed among patients accusing low back pain. Despite most patients being asymptomatic, spondylolisthesis can be the cause of extremely severe, irreversible, and invalidating symptoms. The most frequent types of spondylolisthesis are isthmic, degenerative, dysplastic, traumatic, pathologic, and iatrogenic. While numerous classification systems have been used to describe spondylolisthesis, the descriptive Wiltse classification and the Meyerding classification based on the severity of slippage are the most commonly used in current practice. The clinical evaluation is focused on the degree of pain, the presence of radiculopathy, and other neurological deficits, and is followed by diagnostic imaging, including standing anteroposterior and lateral spine radiographs, computed tomography, and magnetic resonance imaging. Therapeutic options include conserva-tive methods comprising symptomatic medication and physical therapy for lowgrade spondy-lolisthesis with good to excellent results in the long term, as well as surgical treatment with a variety of possible techniques that allow for the reduction of the grade of slippage and obtaining the fusion of the affected level, followed by a well-tailored physical therapy program. The most important role for a patient’s long-term satisfactory result is adequate medical rehabilitation, with consequent and properly designed regimens that the patients will adhere to for the rest of their lives. Keywords: spondylolisthesis; diagnosis; treatment; orthopedics; medical rehabilitation; radiology; magnetic resonance imaging

Is conventional magnetic resonance imaging superior to radiography in the functional integrity evaluation of anterior cruciate ligament in patients with knee osteoarthritis?

BackgroundThe functional integrity of the anterior cruciate ligament (ACL) influences surgical decision-making in patients with knee osteoarthritis (KOA). This study aimed to compare the diagnostic value of radiography and magnetic resonance imaging (MRI) in determining the functional status of ACL.MethodsWe analyzed 306 knees retrospectively using preoperative hip-to-ankle anteroposterior standing (APS) radiographs, anteroposterior (AP) and lateral knee radiographs, AP valgus stress (VS) force radiographs, and standard orthogonal MRI. Based on the intraoperative visualization, the knees were grouped into ACL functionally-intact and ACL functionally-deficient (ACLD) groups. The diagnostic validity and reliability were calculated based on the radiograph parameters such as hip-knee-ankle angle (HKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), posterior tibial slope (PTS), sagittal tibiofemoral subluxation (STFS), coronal tibiofemoral subluxation (CTFS), joint line convergence angle (JLCA), the maximum wear point of the proximal tibia plateau (MWPPT%), and MRI parameters including ACL grades and MWPPT%.ResultsHKA, MPTA, PTS, STFS, JLCA, and CTFS on APS and AP radiographs, and MWPPT% on radiographs and MRI showed a significant diagnostic value (P < 0.05). There were no statistically significant differences in the single parameters from radiographs and MRI. After constructing the logistic regression models, MRI showed higher sensitivity, specificity, and accuracy, reaching 96.8%, 79.9%, and 83.3%, respectively (P < 0.001).ConclusionsIn patients with KOA, the diagnostic value of single radiographic or MRI parameter in assessing the functional integrity of the ACL are equivalent. However, by constructing predictive models, MRI could significantly improve diagnostic validity compared with radiography.

Deep learning for osteoporosis screening using an anteroposterior hip radiograph image.

Osteoporosis is a common bone disorder characterized by decreased bone mineral density (BMD) and increased bone fragility, which can lead to fractures and eventually cause morbidity and mortality. It is of great concern that the one-year mortality rate for osteoporotic hip fractures could be as high as 22%, regardless of the treatment. Currently, BMD measurement is the standard method for osteoporosis diagnosis, but it is costly and requires special equipment. While a plain radiograph can be obtained more simply and inexpensively, it is not used for diagnosis. Deep learning technologies had been applied to various medical contexts, yet few to osteoporosis unless they were trained on the advanced investigative images, such as computed tomography. The purpose of this study was to develop a deep learning model using the anteroposterior hip radiograph images and measure its diagnostic accuracy for osteoporosis. We retrospectively collected all anteroposterior hip radiograph images of patients from 2013 to 2021 at a tertiary care hospital. The BMD measurements of the included patients were reviewed, and the radiograph images that had a time interval of more than two years from the measurements were excluded. All images were randomized using a computer-generated unequal allocation into two datasets, i.e., 80% of images were used for the training dataset and the remaining 20% for the test dataset. The T score of BMD obtained from the ipsilateral femoral neck of the same patient closest to the date of the performed radiograph was chosen. The T score cutoff value of - 2.5 was used to diagnose osteoporosis. Five deep learning models were trained on the training dataset, and their diagnostic performances were evaluated using the test dataset. Finally, the best model was determined by the area under the curves (AUC). A total of 363 anteroposterior hip radiograph images were identified. The average time interval between the performed radiograph and the BMD measurement was 6.6months. Two-hundred-thirteen images were labeled as non-osteoporosis (T score > - 2.5), and the other 150 images as osteoporosis (T score ≤ - 2.5). The best-selected deep learning model achieved an AUC of 0.91 and accuracy of 0.82. This study demonstrates the potential of deep learning for osteoporosis screening using anteroposterior hip radiographs. The results suggest that the deep learning model might potentially be used as a screening tool to find patients at risk for osteoporosis to perform further BMD measurement.

Impact of Offset and Leg Length on Functional Outcomes Post-Total Hip Arthroplasty: How Accurate Should Coronal Reconstruction Be?

BackgroundAccurate hip reconstruction is associated with improved biomechanical behavior following total hip arthroplasty (THA). However, whether this is associated with improved patient-reported outcomes (PROs) is unknown. Hypothesis/PurposeThis study aimed to: 1) describe the ability to reconstruct coronal geometry during THA without advanced technology; 2) assess whether restoration of global offset (GO) and leg length (LL) is associated with improved PROs; and 3) investigate whether increased femoral offset (FO) to compensate for reduced acetabular offset (AO) influences PROs. MethodThis was a prospective, multicenter, consecutive cohort study of 500 patients treated with primary THA without robotics or navigation. The Oxford Hip score (OHS) was obtained preoperatively and at 1-year follow-up. Supine anteroposterior pelvic radiographs were analyzed to determine AO, FO, GO, and LL relative to the native contralateral side. Contour plots for ΔOHS based on ΔLL and ΔGO were created, and ΔOHS was calculated within and outside various ranges (±2.5, ±5, or ±10 mm). ResultsIn the operated hip, mean FO increased by 3 ± 6 mm (range, −16 to 27), while AO decreased by 2 ± 4 mm (range, −17 to 10). The contour graph for ±2.5 mm zones showed the best outcomes (ΔOHS >25) with GO and LL centered on 0 ± 2.5 mm (P < .01). However, only 10% achieved such reconstruction. When GO and LL differences were within ±10 mm, ΔOHS was superior when both AO and FO were within ±5 mm (mean: 24 ± 10; range, −5 to 40) compared with when FO was above 5 mm to compensate for a reduction in AO (mean: 22 ± 11; range, −10 to 46; P = .040). ConclusionsThe PROs were associated with biomechanical reconstruction, and the best clinical improvement can be expected when GO and LL differences are both within 2.5 mm. Maintenance of AO is important, as compensation by increasing FO is associated with inferior OHS.

Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture.

Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with anteroposterior wrist radiographs from one hospital between January 2015 and October 2021 were included. The outcomes were automatic segmentation of wrist radiographs, identification of DRF, and classification of DRF (type A, type B, type C). The Unet model and Fast-RCNN model were used for automatic segmentation. The DenseNet121 model and ResNet50 model were applied to DRF identification of DRF. The DenseNet121 model, ResNet50 model, VGG-19 model, and InceptionV3 model were used for DRF classification. The area under the curve (AUC) with 95% confidence interval (CI), accuracy, precision, and F1-score was utilized to assess the effectiveness of the identification and classification models. Of these 2240 participants, 1440 (64.3%) had DRF, of which 701 (48.7%) were type A, 278 (19.3%) were type B, and 461 (32.0%) were type C. Both the Unet model and the Fast-RCNN model showed good segmentation of wrist radiographs. For DRF identification, the AUCs of the DenseNet121 model and the ResNet50 model in the testing set were 0.941 (95%CI: 0.926-0.965) and 0.936 (95%CI: 0.913-0.955), respectively. The AUCs of the DenseNet121 model (testing set) for classification type A, type B, and type C were 0.96, 0.96, and 0.96, respectively. The DenseNet121 model may provide clinicians with a tool for interpreting wrist radiographs.

Decreasing Hip Offset in Total Hip Arthroplasty Results in Decreased Physical Function Scores

BackgroundAcetabular and femoral offset play an important role in total hip arthroplasty (THA) for postoperative stability and biomechanical function. However, it is unknown whether offset impacts patient-reported outcomes (PROs). This study evaluated patients undergoing direct anterior (DA) THA with the hypothesis that patients who have a decrease in hip offset postoperatively would have lower physical function scores and higher pain interference. MethodsThere were 499 patients who underwent DA THA at a single tertiary academic institution who were retrospectively evaluated. Preoperative and postoperative hip offset was measured by 2 reviewers using the Sundsvall method on standing anteroposterior pelvis radiographs. Postoperative changes in hip offset were categorized as increased (> 5 mm), matched (within 5 mm of the preoperative offset measurement), or decreased ( >5 mm). Postoperative PROs with a minimum 1-year follow-up were recorded. A one-way analysis of variance was utilized to compare postoperative pain and PROs between groups. ResultsPatients who had decreased offset had the lowest mean postoperative physical function scores at 39.4 (8.0), followed by the increased offset group at 42.2 (10.4) and the matched offset group at 42.8 (9.8) (P < .01). There were significant differences in postoperative physical function scores between matched offset (42.8) and decreased offset (39.4) groups (P < .01), as well as between increased offset (42.2) and decreased offset (39.4) groups (P = .04). There was no difference between matched and increased offset cohorts. ConclusionsOur data suggests that reducing hip offset may result in worse physical function scores compared to those who have matched or increased hip offset. This should be considered intraoperatively, and efforts should be made to avoid reduced offset even in the presence of hip stability.

A deep learning-based algorithm for automatic detection of perilunate dislocation in frontal wrist radiographs

This study proposes a Deep Learning algorithm to automatically detect perilunate dislocation in anteroposterior wrist radiographs. A total of 374 annotated radiographs, 345 normal and 29 pathological, of skeletally mature adolescents and adults aged ≥16 years were used to train, validate and test two YOLOv8 deep neural models. The training set included 245 normal and 15 pathological radiographs; the pathological training set was supplemented by 240 radiographs obtained by data augmentation. The test set comprised 30 normal and 10 pathological radiographs. The first model was used for detecting the carpal region, and the second for segmenting a region between Gilula’s 2nd and 3rd arcs. The output of the segmentation model, trained multiple times with varying random initial parameter values and augmentations, was then assigned a probability of being normal or pathological through ensemble averaging. In the study dataset, the algorithm achieved an overall F1-score of 0.880: 0.928 in the normal subgroup, with 1.0 precision, and 0.833 in the pathological subgroup, with 1.0 recall (or sensitivity), demonstrating that diagnosis of perilunate dislocation can be improved by automatic analysis of anteroposterior radiographs. Level of evidence: III.

Harmony between spinopelvic mismatch and sagittal hip alignment contributes to upright standing in females: a cross-sectional study.

In upright standing, spinopelvic mismatch is compensated by hip extension. However, few studies have investigated the reciprocal relationship between the sagittal alignment of the hip joints and spinopelvic mismatch during upright standing in humans. Our study aims to investigate (I) the relationship between spinopelvic mismatch and hip extension and (II) whether insufficient hip extension against spinopelvic mismatch, i.e., pelvic incidence (PI)-lumbar lordosis (LL), affects trunk inclination in upright standing. This study was a retrospective cross-sectional study. We included 398 consecutive female patients treated for osteoporosis at our outpatient department between November 2017 and June 2022. Patients with any of the following were excluded from the study: (I) those whose plain whole-spine radiographs did not cover the femurs, (II) those with fractures in the vertebrae or lower extremities, (III) those with a history of surgery of the spine or of the lower extremities, (IV) those with scoliosis with a Cobb angle ≥10° in the anteroposterior radiograph, and (V) those with transitional vertebrae. Sixty-two patients were divided into normal and malalignment groups based on their sagittal spinal alignment. The patients underwent plain whole-spine radiography as a routine examination. A linear approximation between the pelvic femoral angle (PFA), representing hip extension, and PI-LL was obtained in both groups. The optimal PFA of each patient was obtained by substituting the PI-LL into the linear approximation of the normal group. The difference between the optimal and measured PFA was defined as the ΔPFA for each patient. The correlation between the ΔPFA and sagittal vertical axis (SVA) was evaluated in both groups. The PFA and PI-LL were correlated in both groups. The malalignment group had a significantly greater ΔPFA than the normal group. ΔPFA was correlated with SVA only in the malalignment group. The magnitude of the ΔPFA indicated insufficient hip extension to compensate for the spinopelvic mismatch during upright standing.

Clinical Outcomes of a Novel Hybrid Transtibial Technique for Femoral Tunnel Drilling in Anterior Cruciate Ligament Reconstruction: A Large Single-Center Case Series With a Minimum 2-Year Follow-up.

A novel hybrid transtibial (HTT) approach to femoral tunnel drilling in anterior cruciate ligament reconstruction (ACLR) has been developed that circumvents the need for knee hyperflexion and orients the graft in the most anatomic position without sacrificing the tunnel length or aperture. Patients who underwent ACLR utilizing the HTT technique would achieve excellent patient-reported outcome scores and experience low rates of graft failure and reoperations. Case series; Level of evidence, 4. Patients who underwent primary ACLR at a single institution between 2005 and 2020 were retrospectively reviewed. Patients treated with the HTT, anteromedial portal (AMP), and transtibial (TT) approaches were matched based on age, sex, and body mass index ±3 kg/m2. Demographic and surgical data as well as femoral tunnel angle measurements on anteroposterior and lateral radiographs were collected for the 3 groups. However, clinical outcomes were only reported for the HTT group because of concerns of graft heterogeneity. A total of 170 patients (median age, 26.5 years [interquartile range (IQR), 18.0-35.0 years]) who underwent ACLR using the HTT approach were included. The median coronal- and sagittal-plane femoral tunnel angles were 47° (IQR, 42°-53°) and 40° (IQR, 34°-46°), respectively. The sagittal-plane femoral tunnel angles in the HTT group were significantly more horizontal compared with those in the TT group (P < .0001), whereas the coronal-plane femoral tunnel angles in the HTT group were found to be significantly more vertical compared with those in the AMP group (P = .001) and more horizontal compared with those in the TT group (P < .0001). The graft failure and reoperation rates in the HTT group at a minimum 2-year follow-up were 1.8% (3/170) and 4.7% (8/170), respectively. The complication rate was 6.5% (11/170), with the most common complication being subjective stiffness in 7 patients. The median Lysholm score was 89.5 (IQR, 79.0-98.0); the median International Knee Documentation Committee score was 83.9 (IQR, 65.5-90.8); and the median Veterans RAND 12-Item Health Survey physical and mental component summary scores were 55.0 (IQR, 52.6-55.9) and 56.2 (IQR, 49.1-59.3), respectively. ACLR using the HTT technique was associated with low graft retear and revision surgery rates and good patient-reported outcome scores at medium-term follow-up and demonstrated femoral tunnel obliquity on postoperative radiographs that correlated with optimal parameters previously reported in cadaveric and biomechanical studies.

Surgeon's Experience and Accuracy of Preoperative Digital Templating in Primary Total Hip Arthroplasty.

Preoperative planning has become essential in performance of total hip arthroplasty (THA). However, data regarding the effect of the planner's experience on the accuracy of digital preoperative planning is limited. The objective of this study was to assess the accuracy of digital templating in THA based on the surgeon's experience. A retrospective study was conducted. An analysis of 98 anteroposterior pelvic radiographs, which were individually templated by four surgeons (two hip surgeons and two orthopaedic residents) using TraumaCad® digital planning, was performed. A comparison of preoperatively planned sizes with implanted sizes was performed to evaluate the accuracy of predicting component size. The results of preoperative planning performed by hip surgeons and orthopaedic residents were compared for testing of the planner's experience. Femoral stem was precisely predicted in 32.4% of cases, acetabular component in 40.3%, and femoral offset in 76.7%. Prediction of cup size showed greater accuracy than femoral size among all observers. No differences in any variable were observed among the four groups (acetabular cup P=0.07, femoral stem P=0.82, femoral offset P=0.06). All measurements showed good reliability (intraclass correlation coefficient [ICC] acetabular cup: 0.76, ICC femoral stem: 0.79). The results of this study might suggest that even though a surgeon's experience supports improved precision during the planning stage, it should not be restricted only to surgeons with a high level of experience. We consider preoperative planning an essential part of the surgery, which should be included in training for orthopaedics residents.

Relationship between the medial cuneiform bone morphology and the severity of hallux valgus

BackgroundIt has been reported on the relationship between the medial cuneiform bone morphology, especially in terms of obliquity, and the severity of hallux valgus (HV), however, no consensus has been obtained. On the other hand, there are no reports on the relationship between the difference in height between the medial and intermediate cuneiforms and the severity of hallux valgus. The purpose of this study was to clarify the relationship between the medial cuneiform bone morphology and the severity of HV. MethodsThe authors retrospectively analyzed 200 feet of 116 patients who had a weightbearing anteroposterior foot radiograph taken between April 2017 and July 2022 and diagnosed with HV. Measurements included the hallux valgus angle (HVA), the intermetatarsal angle (IMA), the distal medial cuneiform angle (DMCA) and the cuneiform first-second length (C1-2D). HV groups were classified into one of three groups: mild (15 ≦ HVA<30°, 9 < IMA<13°), moderate (30 ≦ HVA<40°, 13 ≦ IMA≦20°) and severe groups (HVA≧40°, IMA>20°), and the relationship to DMCA or the difference in height between the medial and intermediate cuneiforms was analyzed. ResultsA total of 163 feet of 93 patients were included in this study. There were significant correlations between the HVA and the DMCA (r = 0.47, p <0.001) or the C1-2D (r = 0.64, p <0.001). There was no significant difference in DMCA between the mild and moderate groups (p = 0.14). On the other hand, significant differences in C1-2D were observed between the three groups (mild-moderate; p <0.001, moderate-severe; p = 0.03, mild-severe; p <0.001). IMA was also positively correlated with the DMCA (r = 0.30, p <0.001) or the C1-2D (r = 0.47, p <0.001). However, the DMCA (p = 0.07) and the C1-2D (p = 0.39) did not differ significantly from IMA between the moderate and severe groups. ConclusionsThe difference in height between the medial and intermediate cuneiforms, referred to as C1-2D, is closely associated with the HVA. The C1-2D may influence the progression of HV and be a novel radiographic parameter that indicates severity of HV.

Artificial Intelligence vs. Doctors: Diagnosing Necrotizing Enterocolitis on Abdominal Radiographs

BackgroundRadiographic diagnosis of necrotizing enterocolitis (NEC) is challenging. Deep learning models may improve accuracy by recognizing subtle imaging patterns. We hypothesized it would perform with comparable accuracy to that of senior surgical residents. MethodsThis cohort study compiled 494 anteroposterior neonatal abdominal radiographs (214 images NEC, 280 other) and randomly divided them into training, validation, and test sets. Transfer learning was utilized to fine-tune a ResNet-50 deep convolutional neural network (DCNN) pre-trained on ImageNet. Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps visualized image regions of greatest relevance to the pretrained neural network. Senior surgery residents at a single institution examined the test set. Resident and DCNN ability to identify pneumatosis on radiographic images were measured via area under the receiver operating curves (AUROC) and compared using DeLong's method. ResultsThe pretrained neural network achieved AUROC of 0.918 (95% CI, 0.837–0.978) with an accuracy of 87.8% with five false negative and one false positive prediction. Heatmaps confirmed appropriate image region emphasis by the pretrained neural network. Senior surgical residents had a median area under the receiver operating curve of 0.896, ranging from 0.778 (95% CI 0.615–0.941) to 0.991 (95% CI 0.971–0.999) with zero to five false negatives and one to eleven false positive predictions. The deep convolutional neural network performed comparably to each surgical resident's performance (p > 0.05 for all comparisons). ConclusionsA deep convolutional neural network trained to recognize pneumatosis can quickly and accurately assist clinicians in promptly identifying NEC in clinical practice. Level of EvidenceIII (study type: Study of Diagnostic Test, study of nonconsecutive patients without a universally applied “gold standard”)

Medial Calcar Density Measured via Opportunistic Computed Tomography Is Well Represented by the Dorr C Classification.

Introduction The canal-to-calcar isthmus (CC) ratio has been previously correlated with proximal femur osteology, but its relationship with bone density is not well established. Our purpose was to assess the relationship between femoral bone density, measured on opportunistic quantitative CT in Hounsfield units (HU), and CC ratio. Methods A total of 148 THA patients were included. The CC ratio was measured on anteroposterior hip radiographs. Using perioperative CT scans, a 1 cm diameter area was identified on a single mid-coronal slice in the medial calcar just proximal to the intertrochanteric ridge. The mean HU was calculated in this region to represent calcar bone density. Results Twenty-four percent (n = 35) of patients were classified as Dorr A (average CC ratio 0.47 [0.45; 0.48]), 67% (n = 96) as Dorr B (0.62 [0.55; 0.68]), and 11% (n = 17) as Dorr C (0.78 [0.77; 0.80]). There was a significant difference between Dorr A and Dorr C femurs (769 (144) vs. 588 (154) HU) as well as between B and C femurs (718 (166) vs. 588 (154) HU). The CC ratio was correlated with calcar bone density on CT (-0.370). Conclusion CC ratio is correlated with bone density determined by HU measurements on an opportunistic quantitative computed tomography scan, and bone density HU values were able to accurately differentiate bone density in Dorr A and B from Dorr C femurs. These findings suggest that the CC ratio is a reliable measurement to predict bone density in Dorr C femurs. Therefore, arthroplasty surgeons can confidently use the Dorr classification for patients with Dorr C femurs when preoperatively planning for THA.

Artificial intelligence to automatically measure glenoid inclination, humeral alignment, and the lateralization and distalization shoulder angles on postoperative radiographs after reverse shoulder arthroplasty

BackgroundRadiographic evaluation of the implant configuration after reverse shoulder arthroplasty (RSA) is time-consuming and subject to interobserver disagreement. The final configuration is a combination of implant features and surgical execution. Artificial intelligence (AI) algorithms have been shown to perform accurate and efficient analysis of images. The purpose of this study was to develop an AI algorithm to automatically measure glenosphere inclination, humeral component inclination, and the lateralization and distalization shoulder angles (DSAs) on postoperative anteroposterior radiographs after RSA. MethodsThe Digital Imaging and Communications in Medicine files corresponding to postoperative anteroposterior radiographs obtained after implantation of 143 RSAs were retrieved and used in this study. Four angles were analyzed: (1) glenoid inclination angle (GIA, between the central fixation feature of the glenoid and the floor of the supraspinatus fossa), (2) humeral alignment angle (HAA, between the long axis of the humeral shaft and a perpendicular to the metallic bearing of the prosthesis), (3) DSA, and (4) lateralization shoulder angle (LSA). A UNet segmentation model was trained to segment bony and implant elements using manually segmented training (n = 89) and validation (n = 22) images. Then, an image-processing–based pipeline was developed to measure all 4 angles using AI-segmented images. Measures performed by 3 physician observers and the AI algorithm were then completed in 32 additional images. The agreements among human observers and between observers and the AI algorithm were evaluated using intraclass correlation coefficients (ICCs) and absolute differences in degree. ResultsThe ICCs (95% confidence interval) for manual measurements of LSA, DSA, GIA, and HAA were 0.79 (0.55, 0.90), 0.90 (0.80, 0.95), 0.96 (0.93, 0.98), and 0.99 (0.97, 0.99), respectively. The AI algorithm measured the 32 images in the test set in less than 2 minutes. The agreement between observers and the AI algorithm was lowest when measuring the LSA for observer 2, with an ICC of 0.77 (0.52, 0.89), and an absolute difference in degrees (median [interquartile range]) of 5 (4). Better agreements were found between the AI measurements and the average manual measurements: absolute differences in degree for LSA, DSA, GIA, and HAA were 3 (5), 2 (3), 2 (2), and 2 (1), respectively; ICCs for LSA, DSA, GIA, and HAA were 0.89 (0.79, 0.95), 0.96 (0.93, 0.98), 0.85 (0.68, 0.93), and 0.98 (0.95, 0.99), respectively. ConclusionThe AI algorithm developed in this study can automatically measure the GIA, HAA, LSA, and DSA on postoperative anteroposterior radiographs obtained after implantation on RSA.

Morphological features of the acetabulum with coxa profunda in women: a retrospective observational study

BackgroundThe morphology of coxa profunda remains inadequately understood. However, knowledge about the characteristics of the acetabulum in coxa profunda can help to predict pelvic morphology in three dimensions based on radiographic findings, as well as help to diagnose and predict hip pathologies. Therefore, this study aimed to investigate the relationship between the morphological characteristics of the pelvis and coxa profunda.MethodsWe conducted a retrospective analysis including women who had undergone unilateral total hip arthroplasty. Only those with normal hip joint morphology on the opposite side, as evidenced by anteroposterior pelvic radiography showing a distance of ≥ 2 mm between the ilioischial line and acetabular floor, were included. Five parameters related to acetabular anteversion, thickness, and the position of the ilioischial line were measured using axial computed tomography at the central hip joint. The coxa profunda group (n = 39) and control group (n = 34) were compared.ResultsThe mean acetabular anteversion angle was 12.5° ± 4° in the control group and 22.3° ± 5.6° in the coxa profunda group. The mean thickness from the acetabular fossa to the medial wall was 7.5 ± 1.7 mm in the control group and 3.9 ± 1.2 mm in the coxa profunda group. Furthermore, the bony region representing the ilioischial line was positioned more posteriorly in the coxa profunda group than it was in the control group.ConclusionOur findings suggest that coxa profunda in women is associated with anterior acetabular dysplasia and a thin acetabulum, in contrast to previous interpretations of excessive coverage. This insight suggests a conversion of coxa profunda from a finding of pincer-type femoroacetabular impingement to a finding of acetabular dysplasia, a revelation that also draws attention to cup positioning for total hip arthroplasty.

Evaluating pelvic tilt using the pelvic antero-posterior radiographs: A novel method.

Pelvic tilt (PT) is an important parameter for orthopedic surgeries involving hip and spine, typically determined from sagittal pelvic radiographs. However, various challenges can compromise the feasibility of measurement from sagittal imaging, including obscured landmarks, anatomical variations, hardware interference, and limited medical resources. Addressing these challenges and with the aim of reducing radiation exposure to patients, our study developed a novel method to estimate PT from antero-posterior (AP) radiographs, using vertical distances from the pelvic outlet and obturator foramen. We correlated these measurements with PT, defined both anatomically (anterior pelvic plane, PTa) and mechanically (centers of femoral heads and sacral plate, PTm). The study explored creating linear, exponential, and multivariate regression models based on twelve 3D CT-derived pelvic models (six men, six women), simulating AP radiograph projections with controlled PTs. We then validated these models against 105 pairs of patient stereoradiographs. Statistical analysis revealed that combined exponential-linear models yielded the most accurate results, with Pearson correlation coefficients of 0.75 for PTa and 0.77 for PTm, and mean absolute errors of 3.7° ± 2.6° for PTa and 4.5° ± 3.4° for PTm, showing excellent measurement reliability (all ICCs > 0.9) without significant gender discrepancies. In conclusion, this study presents a validated, simple, and accessible method for estimating PT using AP radiograph parameters, supported by the Supporting Information S1: Excel Tool, showing great potential for clinical application in hip and spine procedures.