Glenoid baseplate orientation in reverse shoulder arthroplasty (RSA) influences clinical outcomes, complications, and failure rates. Novel technologies have been produced to decrease performance heterogeneity of low and high-volume surgeons. This study aimed to determine novice and experienced shoulder surgeon's ability to accurately characterise glenoid component orientation in an intra-operative scenario. Glenoid baseplates were implanted in eight fresh frozen cadavers by novice surgical trainees. Glenoid baseplate version, inclination, augment rotation, and superior-inferior centre of rotation (COR) offset were then measured using in-person visual assessments by novice and experienced shoulder surgeons immediately after implantation. Glenoid orientation parameters were then measured using 3D CT scans with digitally reconstructed radiographs (DRRs) by two independent observers. Bland-Altman plots were produced to determine the accuracy of glenoid orientation using standard intraoperative assessment compared to postoperative 3D CT scan results. Visual assessment of glenoid baseplate orientation showed “poor” to “fair” correlation to 3D CT DRR measurements for both novice and experienced surgeon groups for all measured parameters. There was a clinically relevant, large discrepancy between intra-operative visual assessments and 3D CT DRR measurements for all parameters. Errors in visual assessment of up to 19.2 degrees of inclination and 8mm supero-inferior COR offset occurred. Experienced surgeons had greater measurement error than novices for all measured parameters. Intra-operative measurement errors in glenoid placement may reach unacceptable clinical limits. Kinesthetic input during implantation likely improves orientation understanding and has implications for hands-on learning

Diagnostic interpretation error of paediatric musculoskeletal (MSK) radiographs can lead to late presentation of injuries that subsequently require more invasive surgical interventions with increased risks of morbidity. We aimed to determine the radiograph factors that resulted in diagnostic interpretation challenges for emergency physicians reviewing pediatric MSK radiographs. Emergency physicians provided diagnostic interpretations on 1,850 pediatric MSK radiographs via their participation in a web-based education platform. From this data, we derived interpretation difficulty scores for each radiograph using item response theory. We classified each radiograph by body region, diagnosis (fracture/dislocation absent or present), and, where applicable, the specific fracture location(s) and morphology(ies). We compared the interpretation difficulty scores by diagnosis, fracture location, and morphology. An expert panel reviewed the 65 most commonly misdiagnosed radiographs without a fracture/dislocation to identify normal imaging findings that were commonly mistaken for fractures. We included data from 244 emergency physicians, which resulted in 185,653 unique radiograph interpretations, 42,689 (23.0%) of which were diagnostic errors. For humerus, elbow, forearm, wrist, femur, knee, tibia-fibula radiographs, those without a fracture had higher interpretation difficulty scores relative to those with a fracture; the opposite was true for the hand, pelvis, foot, and ankle radiographs (p < 0 .004 for all comparisons). The descriptive review demonstrated that specific normal anatomy, overlapping bones, and external artefact from muscle or skin folds were often mistaken for fractures. There was a significant difference in difficulty score by anatomic locations of the fracture in the elbow, pelvis, and ankle (p < 0 .004 for all comparisons). Ankle and elbow growth plate, fibular avulsion, and humerus condylar were more difficult to diagnose than other fracture patterns (p < 0 .004 for all comparisons). We identified actionable learning opportunities in paediatric MSK radiograph interpretation for emergency physicians. We will use this information to design targeted education to referring emergency physicians and their trainees with an aim to decrease delayed and missed paediatric MSK injuries

Diagnostic interpretation error of paediatric musculoskeletal (MSK) radiographs can lead to late presentation of injuries that subsequently require more invasive surgical interventions with increased risks of morbidity. We aimed to determine the radiograph factors that resulted in diagnostic interpretation challenges for emergency physicians reviewing pediatric MSK radiographs. Emergency physicians provided diagnostic interpretations on 1,850 pediatric MSK radiographs via their participation in a web-based education platform. From this data, we derived interpretation difficulty scores for each radiograph using item response theory. We classified each radiograph by body region, diagnosis (fracture/dislocation absent or present), and, where applicable, the specific fracture location(s) and morphology(ies). We compared the interpretation difficulty scores by diagnosis, fracture location, and morphology. An expert panel reviewed the 65 most commonly misdiagnosed radiographs without a fracture/dislocation to identify normal imaging findings that were commonly mistaken for fractures. We included data from 244 emergency physicians, which resulted in 185,653 unique radiograph interpretations, 42,689 (23.0%) of which were diagnostic errors. For humerus, elbow, forearm, wrist, femur, knee, tibia-fibula radiographs, those without a fracture had higher interpretation difficulty scores relative to those with a fracture; the opposite was true for the hand, pelvis, foot, and ankle radiographs (p < 0 .004 for all comparisons). The descriptive review demonstrated that specific normal anatomy, overlapping bones, and external artefact from muscle or skin folds were often mistaken for fractures. There was a significant difference in difficulty score by anatomic locations of the fracture in the elbow, pelvis, and ankle (p < 0 .004 for all comparisons). Ankle and elbow growth plate, fibular avulsion, and humerus condylar were more difficult to diagnose than other fracture patterns (p < 0 .004 for all comparisons). We identified actionable learning opportunities in paediatric MSK radiograph interpretation for emergency physicians. We will use this information to design targeted education to referring emergency physicians and their trainees with an aim to decrease delayed and missed paediatric MSK injuries

Introduction. The volume of intraoperative blood loss is measured and reported by OR nurses in many hospitals and doctors do not usually measure it by themselves. To measure intraoperative blood loss accurately is such a difficult task that many measurement errors occur due to various factors. However, it is important to obtain a more correct measurement for performing a safe operation and stable anesthesia control. Case report. In total hip arthroplasty (THA) we had experienced massive intraoperative blood loss errors and later identified the two major causes of these errors. One is the excess volume of infusions for irrigation infusions, and the other is the validity and reliability of the scales on infusion containers. To accurately measure intraoperative blood loss, we should know these two important factors of intraoperative blood loss errors. In arthroplasty we use many infusions for irrigation of the operative field. The labeled (nominal) volume of infusion containers do not accurately indicate the volume of infusions in the container. This is even defined by the WHO international pharmacopoeia (pharmaceutical laws), US, EU, and Japanese pharmacopoeia. According to these pharmacopoeia, the actual volume of infusions is (must be) not less than the labeled (nominal) volume. Moreover, the upper limit of excess volume is not regulated so far. This results in all parenteral infusions (i.e., I.V infusion bags, or bottles of saline) having excess volume compared to their respective labeled volumes. We also have verified the accuracy of volume scales on the infusions bags and bottles and found out some products have inaccuracies that we cannot ignore. After inquiring the pharmaceutical companies about the information concerning excess volume of infusions, we discovered that the excess volume is 2–5% higher than the labeled (nominal) volume depending on the product and company. (e.g., One product has around 3140ml in the container labeled 3000ml). Discussion. Detailed information about excess volume of infusions is neither well recognized so far nor is it open to the public. Knowledge about the excess volume of infusions is necessary to acquire the accurate volume of intraoperative blood loss when using large volume of infusions (i.e., above 3 liters) for irrigating the field of operation. In these cases, excess volume in infusions can be large and cannot be ignored. Further investigation revealed intraoperative blood loss errors tend to be greater when irrigating Total Hip Arthroplasty (THA) compared to the Total Knee Arthroplasty (TKA). A large error in the volume of intraoperative blood loss may affect the decision of whether or not to perform a blood transfusion. Conclusions. This presentation highlights two causes of intraoperative blood loss errors; excess volume of infusions and the validity and reliability of scales on infusion containers. This information has not been shared in any known medical publications and has not been written so far on package inserts (i.e. attached document, Labeling, SmPC, interview form)

Typical devices to limit leg length changes rely on a fixed point in the ileum and femur in order to measure leg length changes intraoperatively. The aim of this study is to determine the ideal position for placement of these devices and to identify potential sources of error. Using saw bones the leg length device was attached at four different positions along the iliac crest extending from the ASIS to its midpoint. After marking the femur on the lateral edge of the Greater Trochanter, measurements were taken with gradually increasing leg length from each individual position on the ileum. This was also performed for different degrees of hip flexion. It was determined that when the hip was in an extended position the degree of error was small for all positions along the iliac crest, with a tendency for an increase error the closer the pin is to the ASIS. When the hip is flexed the error is increased with pin positions closer to the ASIS. With a lengthening of 10 mm, minimal leg length changes can be determined using the device. More than 20 mm resulted in significant change using the leg length device. Ideal iliac crest pin position is towards the midpoint of the iliac crest, which will minimise the potential error. Measuring the leg length while the hip is in a neutral position will limit the error and increase the accuracy—thus avoiding unwanted lengthening

Implant alignment in knee arthroplasty has been identified as critical factor for a successful outcome. Human error during the registration process for imageless computer navigation knee arthroplasty directly affects component alignment. This cadaveric study aims to define the error in the registration of the landmarks and the resulting error in component alignment. Five fresh frozen cadaveric limbs including the hemipelvis were used for the study. Five surgeons performed the registration process via a medial parapatellar approach five times. In order to identify the gold standard point, the soft tissues were stripped and the registration was repeated by the senior author. Errors are presented as mm or degrees from the gold standard registration. The error range in the registration of the femoral centre in the coronal plane was 6.5mm laterally to 5.0mm medially (mean: −0.1, SD: 2.7). This resulted in a mechanical axis error of 5.2 degrees valgus to 2.9 degrees varus (mean: 0.1, SD: 1.1). In the sagittal plane this error was between −1.8 degrees (extension) and 2.7 degrees (flexion). The error in the calculation of the tibial mechanical axis ranged from −1.0 (valgus) to 2.3 (varus) degrees in the coronal plane and −3.2 degrees of extension to 1.3 degrees of flexion. Finally the error in calculating the transepicondylar axis was −11.2 to 6.3 degrees of internal rotation (mean: −3.2, SD: 3.9). The error in the registration process of the anatomical landmarks can result in significant malalignment of the components. The error range for the mechanical axis of the femur alone can exceed the 3 degree margin that has been previously been associated with implant longevity. The technique during the registration process is of paramount importance for image free computer navigation. Future research should be directed towards simplifying this process and minimizing the effect of human error

Introduction. Derotation osteotomies are commonly performed in paediatric orthopaedic and limb reconstruction practice. The purpose of this study was to determine whether the use of a digital inclinometer significantly improves the accuracy in attaining the desired correction. Materials & Methods. We designed an electronic survey regarding derotation femoral osteotomy (DFO) including methods of intra-operative angular correction assessment and acceptable margins of error for correction. This was distributed to 28 paediatric orthopaedic surgeons in our region. A DFO model was created, using an anatomic sawbone with foam covering. 8 orthopaedic surgeons each performed two 30-degree DFOs, one using K-wires and visual estimation (VE), and the other using a Digital Inclinometer (DI). Two radiologists reported pre and post procedure rotational profile CT scans to assess the achieved rotational correction. Results. There was a 68% response rate to the survey. The most popular methods of estimating intra-operative correction were reported to be K-wires and rotation marks on bone. The majority of respondents reported that a 6–10 degree margin of error was acceptable for a 30-degree derotation. This was therefore set as the upper limit for acceptable error margin in the simulation study. The mean error in rotation in the VE group of simulated DFO was 19.7 degrees, with error>5 degrees and error>10 degrees in 7 (88%) and 6 (75%) cases respectively. Mean error in DI group was 3.1 degrees, with error>5 degrees in 1 case (13%). Conclusions. Our results show that the compared to conventional techniques, the use of an inclinometer significantly improves the accuracy of femoral de-rotation and significantly reduces the incidence of unacceptable errors in correction. We would suggest that digital inclinometers be used to assess intra-operative correction during derotation osteotomies

Human error is usually evaluated using statistical descriptions during radiographic annotation. The technological advances popularized the “non-human” landmarking techniques, such as deep learning, in which the error is presented in a confidence format that is not comparable to that of the human method. The region-based landmark definition makes an arbitrary “ground truth” point impossible. The differences in patients’ anatomies, radiograph qualities, and scales make the horizontal comparison difficult. There is a demand to quantify the manual landmarking error in a probability format. Taking the measurement of pelvic tilt (PT) as an example, this study recruited 115 sagittal pelvic radiographs for the measurement of two PTs. We proposed a method to unify the scale of images that allows horizontal comparisons of landmarks and calculated the maximum possible error using a density vector. Traditional descriptive statistics were also applied. All measurements showed excellent reliabilities (intraclass correlation coefficients > 0.9). Eighty-four measurements (6.09%) were qualified as wrong landmarks that failed to label the correct locations. Directional bias (systematic error) was identified due to cognitive differences between observers. By removing wrong labels and rotated pelves, the analysis quantified the error density as a “good doctor” performance and found 6.77°-11.76° maximum PT disagreement with 95% data points. The landmarks with excellent reliability still have a chance (at least 6.09% in our case) of making wrong landmark decisions. Identifying skeletal contours is at least 24.64% more accurate than estimating landmark locations. The landmark at a clear skeletal contour is more likely to generate systematic errors. Due to landmark ambiguity, a very careful surgeon measuring PT could make a maximum 11.76° random difference in 95% of cases, serving as a “good doctor benchmark” to qualify good landmarking techniques

Identifying and restoring alignment is a primary aim of total knee arthroplasty (TKA). In the coronal plane, the pre-pathological hip knee angle can be predicted using an arithmetic method (aHKA) by measuring the medial proximal tibial angle (MPTA) and lateral distal femoral angle (aHKA=MPTA - LDFA). The aHKA is shown to be predictive of coronal alignment prior to the onset of osteoarthritis; a useful guide when considering a non-mechanically aligned TKA. The aim of this study is to investigate the intra- and inter-observer accuracy of aHKA measurements on long leg standing radiographs (LLR) and preoperative Mako CT planning scans (CTs). Sixty-eight patients who underwent TKA from 2020–2021 with pre-operative LLR and CTs were included. Three observers (Surgeon, Fellow, Registrar) measured the LDFA and MPTA on LLR and CT independently on three separate occasions, to determine aHKA. Statistical analysis was undertaken with Bland-Altman test and coefficient of repeatability. An average intra-observer measurement error of 3.5° on LLR and 1.73° on CTs for MPTA was detected. Inter-observer errors were 2.74° on LLR and 1.28° on CTs. For LDFA, average intra-observer measurement error was 2.93° on LLR and 2.3° on CTs, with inter-observer errors of 2.31° on LLR and 1.92° on CTs. Average aHKA intra-observer error was 4.8° on LLR and 2.82° on CTs. Inter-observer error of 3.56° for LLR and 2.0° on CTs was measured. The aHKA is reproducible on both LLR and CT. CT measurements are more reproducible both between and within observers. The difference between measurements using LLR and CT is small and hence these two can be considered interchangeable. CT may obviate the need for LLRs and may overcome difficulties associated with positioning, rotation, body habitus and flexion contractures when assessing coronal alignment

Imageless computer navigation systems have the potential to improve acetabular cup position in total hip arthroplasty (THA), thereby reducing the risk of revision surgery. This study aimed to evaluate the accuracy of three alternate registration planes in the supine surgical position generated using imageless navigation for patients undergoing THA via the direct anterior approach (DAA). Fifty-one participants who underwent a primary THA for osteoarthritis were assessed in the supine position using both optical and inertial sensor imageless navigation systems. Three registration planes were recorded: the anterior pelvic plane (APP) method, the anterior superior iliac spines (ASIS) functional method, and the Table Tilt (TT) functional method. Post-operative acetabular cup position was assessed using CT scans and converted to radiographic inclination and anteversion. Two repeated measures analysis of variance (ANOVA) and Bland-Altman plots were used to assess errors and agreement of the final cup position. For inclination, the mean absolute error was lower using the TT functional method (2.4°±1.7°) than the ASIS functional method (2.8°±1.7°, ρ = .17), and the ASIS anatomic method (3.7°±2.1, ρ < .001). For anteversion, the mean absolute error was significantly lower for the TT functional method (2.4°±1.8°) than the ASIS functional method (3.9°±3.2°, ρ = .005), and the ASIS anatomic method (9.1°±6.2°, ρ < .001). All measurements were within ± 10° for the TT method, but not the ASIS functional or APP methods. A functional registration plane is preferable to an anatomic reference plane to measure intra-operative acetabular cup inclination and anteversion accurately. Accuracy may be further improved by registering patient location using their position on the operating table rather than anatomic landmarks, particularly if a tighter target window of ± 5° is desired

The spinopelvic alignment is often assessed via the Pelvic Incidence-Lumbar Lordosis (PI-LL) mismatch. Here we describe and validate a simplified method to evaluating the spinopelvic alignment through the L1-Pelvis angle (L1P). This method is set to reduce the operator error and make the on-film measurement more practicable. 126 standing lateral radiographs of patients presenting for Total Hip Arthroplasty were examined. Three operators were recruited to label 6 landmarks. One operator repeated the landmark selection for intra-operator analysis. We compare PI-LL mismatch obtained via the conventional method, and our simplified method where we estimate this mismatch using PI-LL = L1P - 90°. We also assess the method's reliability and repeatability. We found no significant difference (p > 0.05) between the PI-LL mismatch from the conventional method (mean 0.22° ± 13.6) compared to L1P method (mean 0.0° ± 13.1). The overall average normalised root mean square error (NRMSE) for PI-LL mismatch across all operators is 7.53% (mean -3.3° ± 6.0) and 6.5% (mean -2.9° ± 4.9) for the conventional and L1P method, respectively. In relation to intra-operator repeatability, the correlation coefficients are 0.87 for PI, 0.94 for LL, and 0.96 for L1P. NRMSE between the two measurement sets are PI: 9.96%, LL: 5.97%, and L1P: 4.41%. A similar trend is observed in the absolute error between the two sets of measurements. Results indicate an equivalence in PI-LL measurement between the methods. Reproducibility of the measurements and reliability between operators were improved. Using the L1P angle, the classification of the sagittal spinal deformity found in the literature translates to: normal L1P<100°, mild 100°<L1P<110°, and severe L1P>110°. Surgeons adopting our method should expect a small improvement in reliability and repeatability of their measurements, and a significant improvement of the assessment of the mismatch through the visualisation of the angle L1P

Evaluation of patient specific spinopelvic mobility requires the detection of bony landmarks in lateral functional radiographs. Current manual landmarking methods are inefficient, and subjective. This study proposes a deep learning model to automate landmark detection and derivation of spinopelvic measurements (SPM). A deep learning model was developed using an international multicenter imaging database of 26,109 landmarked preoperative, and postoperative, lateral functional radiographs (HREC: Bellberry: 2020-08-764-A-2). Three functional positions were analysed: 1) standing, 2) contralateral step-up and 3) flexed seated. Landmarks were manually captured and independently verified by qualified engineers during pre-operative planning with additional assistance of 3D computed tomography derived landmarks. Pelvic tilt (PT), sacral slope (SS), and lumbar lordotic angle (LLA) were derived from the predicted landmark coordinates. Interobserver variability was explored in a pilot study, consisting of 9 qualified engineers, annotating three functional images, while blinded to additional 3D information. The dataset was subdivided into 70:20:10 for training, validation, and testing. The model produced a mean absolute error (MAE), for PT, SS, and LLA of 1.7°±3.1°, 3.4°±3.8°, 4.9°±4.5°, respectively. PT MAE values were dependent on functional position: standing 1.2°±1.3°, step 1.7°±4.0°, and seated 2.4°±3.3°, p< 0.001. The mean model prediction time was 0.7 seconds per image. The interobserver 95% confidence interval (CI) for engineer measured PT, SS and LLA (1.9°, 1.9°, 3.1°, respectively) was comparable to the MAE values generated by the model. The model MAE reported comparable performance to the gold standard when blinded to additional 3D information. LLA prediction produced the lowest SPM accuracy potentially due to error propagation from the SS and L1 landmarks. Reduced PT accuracy in step and seated functional positions may be attributed to an increased occlusion of the pubic-symphysis landmark. Our model shows excellent performance when compared against the current gold standard manual annotation process

The minimisation of errors incurred during the learning process is thought to enhance motor learning and improve performance under pressure or in multitasking situations. If this is proven in surgical skills learning, it has the potential to enhance the delivery of surgical education. We aimed to compare errorless and errorful learning using the high-speed burr. Medical students (n=30) were recruited and allocated randomly to an errorless or errorful group. The errorless learning group progressively learnt tasks from easy to difficult on cedar boards simulating bone. The errorful learning group also progressed through the same tasks but not in order of difficulty. Transfer tasks assessed students’ performance of cervical laminoplasty on saw bone models to assess their level of learning from previous stages. During transfer task 2, students completed the procedure under time pressure and in the presence of distractors, in order to simulate real-life stressors in theatre. Accuracy, precision and safety of the procedure were scored by expert opinions from spine surgeons blinded to the grouping of the participants. Both errorless and errorful learners demonstrated improvements in performance with increasing amounts of practice (demonstrated by the decreased time taken for the task as well as improvement in accuracy of the cuts (depth, width and smoothness). The performance of both groups was not impaired by the incorporation of a secondary task which required participants to multitask. No statistically significant difference in performance was noted between the two groups. In contrast to previous research, there was no significant difference between errorless or errorful learning to develop skills with a high-speed, side-cutting burr. In both groups, practical learning during the session has led to improvement in overall performance with the burr relevant to cervical laminoplasty

Glenoid baseplate positioning for reverse total shoulder replacements (rTSR) is key for stability and longevity. 3D planning and image-derived instrumentation (IDI) are techniques for improving implant placement accuracy. This is a single-blinded randomised controlled trial comparing 3D planning with IDI jigs versus 3D planning with conventional instrumentation. Eligible patients were enrolled and had 3D pre-operative planning. They were randomised to either IDI or conventional instrumentation; then underwent their rTSR. 6 weeks post operatively, a CT scan was performed and blinded assessors measured the accuracy of glenoid baseplate position relative to the pre-operative plan. 47 patients were included: 24 with IDI and 23 with conventional instrumentation. The IDI group were more likely to have a guidewire placement within 2mm of the preoperative plan in the superior/inferior plane when compared to the conventional group (p=0.01). The IDI group had a smaller degree of error when the native glenoid retroversion was >10° (p=0.047) when compared to the conventional group. All other parameters (inclination, anterior/posterior plane, glenoids with retroversion <10°) showed no significant difference between the two groups. Both IDI and conventional methods for rTSA placement are very accurate. However, IDI is more accurate for complex glenoid morphology and placement in the superior-inferior plane. Clinically, these two parameters are important and may prevent long term complications of scapular notching or glenoid baseplate loosening. Image-derived instrumentation (IDI) is significantly more accurate in glenoid component placement in the superior/inferior plane compared to conventional instrumentation when using 3D pre-operative planning. Additionally, in complex glenoid morphologies where the native retroversion is >10°, IDI has improved accuracy in glenoid placement compared to conventional instrumentation. IDI is an accurate method for glenoid guidewire and component placement in rTSA

Accurate measurement of pelvic tilt (PT) is critical in diagnosing hip and spine pathologies. Yet a sagittal pelvic radiograph with good quality is not always available. Studies explored the correlation between PT and sacro-femoral-pubic (SFP) angle from anteroposterior (AP) radiographs yet demonstrated conflicting conclusions about its feasibilities. This study aims to perform a cohort-controlled meta-analysis to examine the correlation between the SFP angle and PT and proposes an application range of the method. This study searched PubMed, Embase, Cochrane, and Web of Science databases for studies that evaluated the correlation between SFP angle and PT. The Pearson's correlation coefficient r from studies were tabulated and compared. Pooled r for overall and gender/age (teenage or adult) controlled subgroup were reported using Fisher's Z transformation. Heterogeneity and publication bias were evaluated using Egger's regression test for the funnel plot asymmetry. Eleven studies were recruited, with nine reported r (totalling 1,247 patients). The overall pooled r was 0.61 with high inter-study heterogeneity (I2 = 75.95%). Subgroup analysis showed that the adult group had a higher r than the teenage group (0.70 versus 0.56, p < 0.001). Although statistically insignificant (p = 0.062), the female group showed a higher r than the male group (0.72 versus 0.65). The SFP method must be used with caution and should not be used in the male teenage group. The current studies did not demonstrate that the SFP method was superior to other AP landmarks correlating to PT. Identical heterogeneity was observed among studies, indicating that more ethnicity-segregated and gender-specific subgroup studies might be necessary. More data input analysing the errors will be useful

INTRODUCTION. Mechanical properties mapping based on CT-attenuation is the basis of finite element (FE) modeling with heterogeneous materials and bone geometry defined from clinical-resolution CT scans. Accuracy between empirical and computational models that use constitutive equations relating CT-attenuation to bone density are well described, but material mapping strategy has not gained similar attention. As such, the objective of this study was to determine variations in the apparent modulus of trabecular bone cores mapped with various material mapping strategies, using a validated density-modulus relationship and co-registered µFEMs as the gold standard. METHODS. Micro-CT images (isotropic 32 µm) were used to create µFEMs from glenoid trabecular bone cores of 14 cadaveric scapula. Each µFEM was loaded in unconstrained compression to determine the trabecular core apparent modulus (E. app. ). Quantitative CT (QCT) images (isotropic 0.625 mm) were subsequently acquired and co-registered QCT-FEMs created for each of the 14 cores. The QCT-FEMs were meshed with either linear hexahedral (HEX8), linear tetrahedral (TET4), or quadratic tetrahedral (TET10) elements at 3 mesh densities (0.3125 mm, 0.46875 mm, 0.625 mm). Three material mapping strategies were used to apply heterogeneous element-wise (element-averaging of the native HU field (Mimics V.20, Materialise, Leuven BE)) or nodal (tri-linear interpolation of HU Field or E Field (Matlab V. R2017a, Natick, RI, USA)) material properties to the QCT FEMs. Identical boundary conditions were used and E. app. between the µFEMs and QCT-FEMs was compared (Figure 1). The QCT density of each hexahedral mesh with element size equal to voxel dimensions was used to compare the QCT density mapping between tetrahedral meshes and material mapping strategy. RESULTS. For tetrahedral meshes the mean QCT density error was 2.4±2.7%, 4.3±4.4%, and 1.6±2.5%, for tetrahedral mesh densities of 0.3125, 0.46875, and 0.625 mm, respectively. Nodal material mapping differs by TET4 and TET10 and therefore for tri-linear interpolation the QCT density error was 0.4±1.6%, 3.5±3.3%, and 2.0±2.2%, for TET4 mesh densities of 0.3125, 0.46875, and 0.625 mm, respectively. The errors were −0.6±1.4%, 2.0±1.4%, 0.2±1.9% for TET10 mesh densities of 0.3125, 0.46875, and 0.625 mm, respectively. Percentage errors in E. app. as a function of bone volume fraction (BV/TV) by material mapping strategy were lowest for HEX8 QCT-FEMs mapped with element-based HU (MIMICS). This was also the best mapping strategy for both TET4 and TET10 QCT-FEMs. The node-based material mapping using the HU field was best for TET4 QCT-FEMs with 0.625 mm elements. The node-based E field mapping had the lowest errors for TET10 QCT-FEMs but had greater errors than the other two mapping strategies for all element types (Figure 2). DISCUSSION. This study compared material mapping strategy, element type, and element density in QCT-FEMs compared to co-registered µFEMs. It was found that QCT-FEMs with hexahedral elements most closely match µFEMs when element averaging of the native HU field is used. This mapping strategy also showed relatively lower errors with linear and quadratic tetrahedral elements compared to node-based material mapping strategies. If modeling parameters are carefully considered when developing QCT-FEMs, models have the potential to accurately replicate micro-level trabecular bone apparent properties. For any figures or tables, please contact the authors directly

To explore a novel machine learning model to evaluate the vertebral fracture risk using Decision Tree model and train the model by Bone Mineral Density (BMD) of different compartments of vertebral body. We collected a Computed Tomography image dataset, including 10 patients with osteoporotic fracture and 10 patients without osteoporotic fracture. 40 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients with osteoporotic fracture in the CT database and 53 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients without osteoporotic fracture in the CT database. Based on the biomechanical properties, 93 vertebral bodies were further segmented into 11 compartments: eight trabecular bone, cortical shell, top and bottom endplate. BMD of these 11 compartments was calculated based on the HU value in CT images. Decision tree model was used to build fracture prediction model, and Support Vector Machine was built as a compared model. All BMD data was shuffled to a random order. 70% of data was used as training data, and 30% left was used as test data. Then, training prediction accuracy and testing prediction accuracy were calculated separately in the two models. The training accuracy of Decision Tree model is 100% and testing accuracy is 92.14% after trained by BMD data of 11 compartments of the vertebral body. The type I error is 7.14% and type II error is 0%. The training accuracy of Support Vector Machine model is 100% and the testing accuracy is 78.57%. The type I error is 17.86% and type II error is 3.57%. The performance of vertebral body fracture prediction using Decision Tree is significantly higher than using Support Vector Machine. The Decision Tree model is a potential risk assessment method for clinical application. The pilot evidence showed that Decision Tree prediction model overcomes the overfitting drawback of Support Vector Machine Model. However, larger dataset and cohort study should be conducted for further evidence

Single level discectomy (SLD) is one of the most commonly performed spinal surgery procedures. Two key drivers of their cost-of-care are duration of surgery (DOS) and postoperative length of stay (LOS). Therefore, the ability to preoperatively predict SLD DOS and LOS has substantial implications for both hospital and healthcare system finances, scheduling and resource allocation. As such, the goal of this study was to predict DOS and LOS for SLD using machine learning models (MLMs) constructed on preoperative factors using a large North American database. The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for SLD procedures from 2014-2019. The dataset was split in a 60/20/20 ratio of training/validation/testing based on year. Various MLMs (traditional regression models, tree-based models, and multilayer perceptron neural networks) were used and evaluated according to 1) mean squared error (MSE), 2) buffer accuracy (the number of times the predicted target was within a predesignated buffer), and 3) classification accuracy (the number of times the correct class was predicted by the models). To ensure real world applicability, the results of the models were compared to a mean regressor model. A total of 11,525 patients were included in this study. During validation, the neural network model (NNM) had the best MSEs for DOS (0.99) and LOS (0.67). During testing, the NNM had the best MSEs for DOS (0.89) and LOS (0.65). The NNM yielded the best 30-minute buffer accuracy for DOS (70.9%) and ≤120 min, >120 min classification accuracy (86.8%). The NNM had the best 1-day buffer accuracy for LOS (84.5%) and ≤2 days, >2 days classification accuracy (94.6%). All models were more accurate than the mean regressors for both DOS and LOS predictions. We successfully demonstrated that MLMs can be used to accurately predict the DOS and LOS of SLD based on preoperative factors. This big-data application has significant practical implications with respect to surgical scheduling and inpatient bedflow, as well as major implications for both private and publicly funded healthcare systems. Incorporating this artificial intelligence technique in real-time hospital operations would be enhanced by including institution-specific operational factors such as surgical team and operating room workflow

Introduction. Current methodologies for designing and validating existing THA systems can be expensive and time-consuming. A validated mathematical model provides an alternative solution with immediate predictions of contact mechanics and an understanding of potential adverse effects. The objective of this study is to demonstrate the value of a validated forward solution mathematical model of the hip that can offer kinematic results similar to fluoroscopy and forces similar to telemetric implants. Methods. This model is a forward solution dynamic model of the hip that incorporates the muscles at the hip, the hip capsule, and the ability to modify implant position, orientation, and surgical technique. Muscle forces are simulated to drive the motion, and a unique contact detection algorithm allows for virtual implantation of components in any orientation. Patient-specific data was input into the model for a telemetric subject and for a fluoroscopic subject. Results. For both stance and swing phase, the model predicted similar patterns and magnitudes compared to telemetry (forces) and fluoroscopy (kinematics). During stance phase, the model predicts 2.5 xBW of maximum hip force while telemetry predicts 2.3 xBW, yielding 8.7% error (Figure 1a). During swing phase, the model predicts 1.1 xBW maximum hip force, similar to telemetry (Figure 1b). During stance phase, the model predicts 1.3mm of hip separation (sliding) compared to 1.6mm for fluoroscopy, yielding 18.8% error (Figure 1c). During swing phase, the model predicts 1.9mm of separation compared to 1.7mm for fluoroscopy, yielding 11.8% error (Figure 1d). The model was also used to assess component placement, version, and optimal positioning compared to live surgery, producing very promising results. Conclusion. The model has proven accurate in predicting kinematics and forces. Therefore, forward solution mathematical modeling can be used to efficiently evaluate new component designs, positioning and technique differences, patient-specific scenarios, and any specific contribution towards THA outcomes that cannot be controlled in vivo. For any figures or tables, please contact the authors directly

In the current healthcare environment, cost containment has become more important than ever. Perioperative services are often scrutinized as they consume more than 30% of North American hospitals’ budgets. The procurement, processing, and use of sterile surgical inventory is a major component of the perioperative care budget and has been recognized as an area of operational inefficiency. Although a recent systematic review supported the optimization of surgical inventory reprocessing as a means to increase efficiency and eliminate waste, there is a paucity of data on how to actually implement this change. A well-studied and established approach to implementing organizational change is Kotter's Change Model (KCM). The KCM process posits that organizational change can be facilitated by a dynamic 8-step approach and has been increasingly applied to the healthcare setting to facilitate the implementation of quality improvement (QI) interventions. We performed an inventory optimization (IO) to improve inventory and instrument reprocessing efficiency for the purpose of cost containment using the KCM framework. The purpose of this quality improvement (QI) project was to implement the IO using KCM, overcome organizational barriers to change, and measure key outcome metrics related to surgical inventory and corresponding clinician satisfaction. We hypothesized that the KCM would be an effective method of implementing the IO. This study was conducted at a tertiary academic hospital across the four highest-volume surgical services - Orthopedics, Otolaryngology, General Surgery, and Gynecology. The IO was implemented using the steps outlined by KCM (Figure 1): 1) create coalition, 2) create vision for change, 3) establish urgency, 4) communicate the vision, 5) empower broad based action, 6) generate general short term wins, 7) consolidate gains, and 8) anchor change. This process was evaluated using inventory metrics - total inventory reduction and depreciation cost savings; operational efficiency metrics - reprocessing labor efficiency and case cancellation rate; and clinician satisfaction. The implementation of KCM is described in Table 1. Total inventory was reduced by 37.7% with an average tray size reduction of 18.0%. This led to a total reprocessing time savings of 1333 hours per annum and labour cost savings of $39 995 per annum. Depreciation cost savings was $64 320 per annum. Case cancellation rate due to instrument-related errors decreased from 3.9% to 0.2%. The proportion of staff completely satisfied with the inventory was 1.7% pre-IO and 80% post-IO. This was the first study to show the success of applying KCM to facilitate change in the perioperative setting with respect to surgical inventory. We have outlined the important organizational obstacles faced when making changes to surgical inventory. The same KCM protocol can be followed for optimization processes for disposable versus reusable surgical device purchasing or perioperative scheduling. Although increasing efforts are being dedicated to quality improvement and efficiency, institutions will need an organized and systematic approach such as the KCM to successfully enact changes. For any figures or tables, please contact the authors directly