Median Percentage Error Research Articles

Blood on the drapes: A multispecialty comparison of blood spill estimates.

Estimated blood loss (EBL) is an important part of the perioperative process. This project aims to determine the accuracy of perioperative team members to estimate blood volume on drapes and the operating room floor. Aliquots of unused human blood were used to create surgical scenarios, and standardized pictures and videos were taken. Physicians, residents, nurses, medical students, and surgical technicians were surveyed and asked to estimate the blood volume for each series. Accuracy and consistency of responses was analyzed. One hundred and forty five responses were recorded: 57 attending physicians, 36 residents, 27 registered nurses, 17 medical students, and seven circulating surgical techs. Median percent error (PE) for all cases was 211.11%, demonstrating a global overestimation of blood volume. PE for the 150mL images was statistically significantly lower than that of the 50 and 100mL images. Circulating Surgical Technicians were the most accurate group, with a median PE of 125%, followed closely by Medical Students (PE=158.33%). The most accurate specialty was Orthopedics (PE=168.06%). The least accurate groups were Attending Physicians (PE=286.11%) and General surgery (GSGY) (PE=327.78%). The most accurate orthopedic surgery and GSGY subspecialties were Hand (PE=237.64%) and Vascular (PE=108.33%), respectively. Statistical analyses showed no significant differences by clinical role, surgical specialty, or subspecialty. This study demonstrates a global overestimation of blood volume when using the visual method, with improved accuracy at higher volumes. Our findings highlight the limitations of visual estimation methods for EBL.

Estimation of Nasal Airway Cross-sectional Area From Endoscopy Using Depth Maps: A Proof-of-Concept Study.

Endoscopy is routinely used to diagnose obstructive airway diseases. Currently, endoscopy is only a visualization technique and does not allow quantification of airspace cross-sectional areas (CSAs). This pilot study tested the hypothesis that CSAs can be accurately estimated from depth maps created from virtual endoscopy videos. Cross-sectional. Academic tertiary medical center. Virtual endoscopy and depth map videos of the nasal cavity were digitally created based on anatomically accurate three-dimensional (3D) models built from computed tomography scans of 30 subjects. A software tool was developed to outline the airway perimeter and estimate the airspace CSA from the depth maps. Two otolaryngologists used the software tool to estimate the nasopharynx CSA and the nasal valve minimal CSA (mCSA) in the left and right nasal cavities. Model validation statistics were performed. Nasopharynx CSA had a median percent error of 3.7% to 4.6% when compared to the true values measured in the 3D models. Nasal valve mCSA had a median percent error of 22.7% to 33.6% relative to the true values. Raters successfully used the software tool to identify subjects with nasal valve stenosis (ie, mCSA < 0.20 cm2) with a sensitivity of 83.3%, specificity ≥ 90.7%, and classification accuracy ≥ 90.0%. Interrater and intrarater agreements were high. This study demonstrates that airway CSAs in 3D models can be accurately estimated from depth maps. The development of artificial intelligence algorithms to compute depth maps may soon allow the quantification of airspace CSAs from clinical endoscopies.

Six-minute, in vivo MRI quantification of proximal femur trabecular bone 3D microstructure.

Assessment of proximal femur trabecular bone microstructure in vivo by magnetic resonance imaging has recently been validated for acquiring information independent of bone mineral density in osteoporotic patients. However, the requisite signal-to-noise ratio (SNR) and resolution for interrogation of the trabecular microstructure at this anatomical location prolongs the scan duration and renders the imaging protocol clinically infeasible. Parallel imaging and compressed sensing (PICS) techniques can reduce the scan duration of the imaging protocol without substantially compromising image quality. The present work investigates the limits of acceleration for a commonly used PICS technique, ℓ1-ESPIRiT, for the purpose of quantifying measures of trabecular bone microarchitecture. Based on a desired error tolerance, a six-minute, prospectively accelerated variant of the imaging protocol was developed and assessed for intersession reproducibility and agreement with the longer reference scan. To investigate the limits of acceleration for MRI-based trabecular bone quantification by parallel imaging and compressed sensing reconstruction, and to develop a prototypical imaging protocol for assessing the proximal femur microstructure in a clinically practical scan time. Healthy participants (n=11) were scanned by a 3D balanced steady-state free precession (bSSFP) sequence satisfying the Nyquist criterion with a scan duration of about 18min. The raw data were retrospectively undersampled and reconstructed to mimic various acceleration factors ranging from 2 to 6. Trabecular volumes-of-interest in four major femoral regions (greater trochanter, intertrochanteric region, femoral neck, and femoral head) were analyzed and six relevant measures of trabecular bone microarchitecture (bone volume fraction, surface-to-curve ratio, erosion index, elastic modulus, trabecular thickness, plates-to-rods ratio) were obtained for images of all accelerations. To assess agreement, median percent error and intraclass correlation coefficients (ICCs) were computed using the fully-sampled data as reference. Based on this analysis, a prospectively 3-fold accelerated sequence with a duration of about 6min was developed and the analysis was repeated. A prospective acceleration factor of 3 demonstrated comparable performance in reproducibility and absolute agreement to the fully-sampled scan. The median CoV over all image-derived metrics was generally <6% and ICCs >0.70. Also, measurements from prospectively 3-fold accelerated scans demonstrated in general median percent errors of <7% and ICCs >0.70. The present work proposes a method to make in vivo quantitative assessment of proximal femur trabecular microstructure with a clinically practical scan duration of about 6min.

Valuation accuracy in the residential property markets in Minna and Abuja, Nigeria

PurposeThe rising concern for the accuracy of residential valuations in Nigeria has created the need for key stakeholders in the residential property markets in the study areas to know the level of accuracy of valuations in order to make rational residential property transactions, amongst other purposes.Design/methodology/approachA blend of descriptive and causal designs was adopted for the study. Data were collected via structured questionnaire administered to 179 estate surveying and valuation (ESV) firms in the study areas using census sampling technique. Analytical techniques such as median percentage error (PE), mean and relative importance index (RII) analysis were employed in the analysis of data collected for the study.FindingsThe study found that valuation accuracy is greater in the residential property market in Abuja than in Minna, with inappropriate valuation methodology as the most significant cause of valuation inaccuracy.Practical implicationsThe practical implication of this study is that a reliable databank should be established for the property market to provide credible transaction data for valuers to conduct accurate valuations in these cities. Strict enforcement of national and international valuation standards by the regulatory authorities as well as retraining of valuers on appropriate application of valuation approaches and methods are the recommended corrective measures.Originality/valueNo study has comparatively examined the accuracy of valuations in two extremely different residential property markets in the country using actual valuation and transaction prices.

Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference.

Measurement of colorectal polyp size during endoscopy is mainly performed visually. In this work, we propose a novel polyp size measurement system (Poseidon) based on artificial intelligence (AI) using the auxiliary waterjet as a measurement reference. Visual estimation, biopsy forceps-based estimation, and Poseidon were compared using a computed tomography colonography-based silicone model with 28 polyps of defined sizes. Four experienced gastroenterologists estimated polyp sizes visually and with biopsy forceps. Furthermore, the gastroenterologists recorded images of each polyp with the waterjet in proximity for the application of Poseidon. Additionally, Poseidon's measurements of 29 colorectal polyps during routine clinical practice were compared with visual estimates. In the silicone model, visual estimation had the largest median percentage error of 25.1 % (95 %CI 19.1 %-30.4 %), followed by biopsy forceps-based estimation: median 20.0 % (95 %CI 14.4 %-25.6 %). Poseidon gave a significantly lower median percentage error of 7.4 % (95 %CI 5.0 %-9.4 %) compared with other methods. During routine colonoscopies, Poseidon presented a significantly lower median percentage error (7.7 %, 95 %CI 6.1 %-9.3 %) than visual estimation (22.1 %, 95 %CI 15.1 %-26.9 %). In this work, we present a novel AI-based method for measuring colorectal polyp size with significantly higher accuracy than other common sizing methods.

Development and Validation of a Weight Estimation Tool for Acutely Ill Children Who Cannot be Weighed.

To develop and validate weight estimation tools using mid upper arm circumference (MUAC) and body length, and determine accuracy and precision of Broselow tape in children 6 mo to 15 y of age. Data of 18,456 children aged 6 mo to 5 y and 1420 children aged 5 to 15 y were used to develop linear regression equations using length and MUAC to estimate weight. These were validated on prospectively enrolled populations of 276 and 312 children, respectively. Accuracy was measured by Bland-Altman bias, median percentage errors, and percentage of predicted weight within 10% of true weight. Broselow tape was tested on the validation population. Gender specific equations were developed which estimated weight within 10% of true weight in 69.9% (64.1-75.2%) and 65.7% (60.1-70.9%) of children aged 6 mo to 5 y, and 5 to 15 y, respectively. Broselow tape predicted weight within 10% of the true weight in 40.5% (34.7-46.6%) and 32.5% (26.7-38.7%) of children aged 6 mo to 5 y and 5 to 15 y, respectively. The model developed from MUAC and length accurately estimated weight in children aged 6 mo to 15 y, and is potentially useful during emergencies. The Broselow tape frequently overestimated weight in authors' setting.

Machine Learning of Usable Area of Gable-Roof Residential Buildings Based on Topographic Data

In real estate appraisal, especially of residential buildings, one of the primary evaluation parameters is the property’s usable area. When determining the property price, Polish appraisers use data from comparable transactions included in the Real Estate Price Register (REPR), which is highly incomplete, especially regarding properties’ usable areas. This incompleteness renders the identification of comparable transactions challenging and may lead to incorrect prediction of the property price. We address this challenge by applying machine learning methods to estimate the usable area of buildings with gable roofs based only on their topographic data, which is widely available in Poland in the Database of Topographic Objects (BDOT10k) of Light Detection and Ranging (LiDAR) origin. We show that three features are enough to make accurate predictions of the usable area: the covered area, the building’s height, and the number of stories optionally. A neural network trained on buildings from architectural bureaus reached a 4% median percentage error on the same source and 15% on the real buildings from the city of Koszalin, Poland. Therefore, the proposed method can be applied by appraisers to estimate the usable area of buildings with known transaction prices and solve the problem of finding comparable properties for appraisal.

Quantification and dosimetry of small volumes including associated uncertainty estimation

BackgroundAccurate quantification of radioactivity in a source of interest relies on accurate registration between SPECT and anatomical images, and appropriate correction of partial volume effects (PVEs). For small volumes, exact registration between the two imaging modalities and recovery factors used to correct for PVE are unreliable. There is currently no guidance relating to quantification or the associated uncertainty estimation for small volumes.Material and methodsA method for quantification of small sources of interest is proposed, which uses multiple oversized volumes of interest. The method was applied to three Na[131I]I activity distributions where a Na[131I]I capsule was situated within a cylindrical phantom containing either zero background, uniform background or non-uniform background and to a scenario with small lesions placed in an anthropomorphic phantom. The Na[131I]I capsule and lesions were quantified using the proposed method and compared with measurements made using two alternative quantification methods. The proposed method was also applied to assess the absorbed dose delivered to a bone metastasis following [131I]mIBG therapy for neuroblastoma including the associated uncertainty estimation.ResultsThe method is accurate across a range of activities and in varied radioactivity distributions. Median percentage errors using the proposed method in no background, uniform backgrounds and non-uniform backgrounds were − 0.4%, − 0.3% and 1.7% with median associated uncertainties of 1.4%, 1.4% and 1.6%, respectively. The technique is more accurate and robust when compared to currently available alternative methods.ConclusionsThe proposed method provides a reliable and accurate method for quantification of sources of interest, which are less than three times the spatial resolution of the imaging system. The method may be of use in absorbed dose calculation in cases of bone metastasis, lung metastasis or thyroid remnants.

Comparative Prediction of Gas Chromatographic Retention Indices for GC/MS Identification of Chemicals Related to Chemical Weapons Convention by Incremental and Machine Learning Methods

During on-site verification activities conducted by the Technical Secretariat of Organization for the Prohibition of Chemical Weapons, identification by gas chromatography retention indices (RI) data, in addition to mass spectrometry data, increase the reliability of factual findings. However, reference RIs do not cover all the possible chemical structures. That is why it is important to have models to predict RIs. Applicable only for narrow data sets of chemicals with a fixed scaffold (G- and V-series gases as example), the non-learning incremental method demonstrated predictive median absolute and percentage errors of 2–4 units and 0.1–0.2%; these are comparable with the experimental bias in RI measurements in the same laboratory with the same GC conditions. It outperforms the accuracy of two reported machine learning methods–median absolute and percentage errors of 11–52 units and 0.5–2.8%. However, for the whole Chemical Weapons Convention (CWC) data set of chemicals, when a fixed scaffold is absent, the incremental method is not applicable; essential machine learning methods achieved accuracy: median absolute and percentage errors of 29–33 units and 0.5–2.2%, depending on the machine learning method. In addition, we have developed a homology tree approach as a convenient method for the visualization of the CWC chemical space. We conclude that non-learning incremental methods may be more accurate than the state-of-the-art machine learning techniques in particular cases, such as predicting the RIs of homologues and isomers of chemicals related to CWC.

Evaluation of Select Velocity Measurement Techniques for Estimating Discharge in Small Streams across the United States

ABSTRACTMultiple instruments and methods exist for collecting discrete streamflow measurements in small streams with low flows, defined here as less than 5.7 m3/s (200 ft3/s). Included in the available methods are low‐cost approaches that are infrequently used, in part, because their uncertainty is not well known. In this work, we evaluated the accuracy and suitability of three low‐cost velocity measurement methods (surface float [SF], velocity head rod [VR], and rising body [RB]) and three conventional current meters (acoustic Doppler velocimeter, and mechanical Price type AA and Price Pygmy meters) relative to discharge calculated from stable artificial hydraulic controls. A total of 231 measurements were made by 20 individuals during 88 site visits to 24 sites in eight states. Accuracies were assessed for all methods and precision was evaluated for the low‐cost methods. The median percent error was below 5% for conventional methods, and below 20% for the low‐cost methods. The SF was the most accurate (median absolute percent error 14%) and precise (mean percent precision of 11%) low‐cost method. The RB and VR, respectively, had 15% and 20% median absolute percent error and 29% and 12% mean percent precision. Results suggest that low‐cost methods, when used appropriately, can be used to estimate discharge data under low flow conditions when measurements with conventional methods are not feasible and the associated accuracies meet end‐user measurement objectives.

Historical and future trends in emergency pituitary referrals: a machine learning analysis

PurposeAcute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use.MethodsA data-driven analysis was performed using all available electronic neurosurgical referrals (2014–2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance.Results462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, female:male = 0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic.ConclusionThis work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement.

Volumetric measurement of intracranial meningiomas: a comparison between linear, planimetric, and machine learning with multiparametric voxel-based morphometry methods.

To compare the accuracy of three volumetric methods in the radiological assessment of meningiomas: linear (ABC/2), planimetric, and multiparametric machine learning-based semiautomated voxel-based morphometry (VBM), and to investigate the relevance of tumor shape in volumetric error. Retrospective imaging database analysis at the authors' institutions. We included patients with a confirmed diagnosis of meningioma and preoperative cranial magnetic resonance imaging eligible for volumetric analyses. After tumor segmentation, images underwent automated computation of shape properties such as sphericity, roundness, flatness, and elongation. Sixty-nine patients (85 tumors) were included. Tumor volumes were significantly different using linear (13.82 cm3 [range 0.13-163.74 cm3]), planimetric (11.66 cm3 [range 0.17-196.2 cm3]) and VBM methods (10.24 cm3 [range 0.17-190.32 cm3]) (p < 0.001). Median volume and percentage errors between the planimetric and linear methods and the VBM method were 1.08 cm3 and 11.61%, and 0.23 cm3 and 5.5%, respectively. Planimetry and linear methods overestimated the actual volume in 79% and 63% of the patients, respectively. Correlation studies showed excellent reliability and volumetric agreement between manual- and computer-based methods. Larger and flatter tumors had greater accuracy on planimetry, whereas less rounded tumors contributed negatively to the accuracy of the linear method. Semiautomated VBM volumetry for meningiomas is not influenced by tumor shape properties, whereas planimetry and linear methods tend to overestimate tumor volume. Furthermore, it is necessary to consider tumor roundness prior to linear measurement so as to choose the most appropriate method for each patient on an individual basis.

Using Deep Learning to Fill Data Gaps in Environmental Footprint Accounting.

Environmental footprint accounting relies on economic input-output (IO) models. However, the compilation of IO models is costly and time-consuming, leading to the lack of timely detailed IO data. The RAS method is traditionally used to predict future IO tables but suffers from doubts for unreliable estimations. Here we develop a machine learning-augmented method to improve the accuracy of the prediction of IO tables using the US summary-level tables as a demonstration. The model is constructed by combining the RAS method with a deep neural network (DNN) model in which the RAS method provides a baseline prediction and the DNN model makes further improvements on the areas where RAS tended to have poor performance. Our results show that the DNN model can significantly improve the performance on those areas in IO tables for short-term prediction (one year) where RAS alone has poor performance, R2 improved from 0.6412 to 0.8726, and median APE decreased from 37.49% to 11.35%. For long-term prediction (5 years), the improvements are even more significant where the R2 is improved from 0.5271 to 0.7893 and median average percentage error is decreased from 51.12% to 18.26%. Our case study on evaluating the US carbon footprint accounts based on the estimated IO table also demonstrates the applicability of the model. Our method can help generate timely IO tables to provide fundamental data for a variety of environmental footprint analyses.

Quantitative water content estimation in landfills through joint inversion of seismic refraction and electrical resistivity data considering surface conduction

The disposal of municipal solid waste (MSW) in landfills is the prevalent method of waste management at the global scale. However, the production of landfill gases due to the methanogenic fermentation of wet MSW is a possible threat to human health and accounts for a substantial contribution to the global greenhouse gas emissions. Accordingly, information regarding water content is critical as it is an important factor triggering methane production in MSW landfills. In this study, we propose a petrophysical joint inversion scheme to quantitatively solve for the water content (WC) in landfills based on seismic refraction as well as electrical resistivity data collected at two different frequencies. In this way, we also take into account the contribution of the surface conductivity to the observed electrical response, which is crucial for a reliable quantification of the WC. Our results reveal a high water content within the MSW unit (WC > 20%) for areas characterized by a strong polarization response (normalized chargeability > 5 Mn mS/m). Such areas can be related to an increased biogeochemical activity as evidenced by the detected methane production. We observe consistent estimates between the water content resolved through the proposed joint inversion scheme and values measured in waste samples with a median percentage error of 17%. Our study demonstrates the possibility to obtain reliable estimates for the WC in MSW landfills through the petrophysical joint inversion of seismic and electrical data when surface conductivity is explicitly considered.

Ultrasound estimation of fetal weight accuracy in a regional hospital: A retrospective audit

AbstractIntroductionUltrasound estimated fetal weight (EFW) is a vital part of prenatal care. We aimed to determine the accuracy of sonographic EFW at a regional Australian hospital, explore factors associated with EFW accuracy and review accuracy calculation methods.MethodsUltrasound EFW (Hadlock C) was calculated within one week of a live delivery, for 357 singleton pregnancies. EFW accuracy, the difference between EFW, and live birthweight (LBW), were quantified using percentage error (PE); absolute error (AE); ‘signed’ error and AE ≤10% of LBW.ResultsEstimations of accuracy varied by calculation method, with PE smaller (mean 2.3% [CI 1.5, 3.1], median PE 2.4% [IQR −3.4, 6.7]) than AE (mean 6.4% [CI 5.9, 6.9], median AE 5.1% [IQR 2.7, 9.0]). Signed error indicated the majority of EFWs were overestimates and 80.1% of EFW calculations had an AE within 10% of LBW. EFW inaccuracy (AE ≥10% of LBW) varied between sonographers (p = .023) and scan to delivery interval (p = .003).ConclusionsEFW accuracy rates at the regional hospital were similar to published error rates. EFW accuracy varied with sonographer and with time from scan to delivery. Absolute error provides more useful and comparable information than percentage error of EFW.

Validity and Acceptability of Wearable Devices for Monitoring Step-Count and Activity Minutes Among People With Multiple Sclerosis.

Multiple wearable devices that purport to measure physical activity are widely available to consumers. While they may support increases in physical activity among people with multiple sclerosis (MS) by providing feedback on their performance, there is little information about the validity and acceptability of these devices. Providing devices that are perceived as inaccurate and difficult to use may have negative consequences for people with MS, rather than supporting participation in physical activity. The aim of this study was, therefore, to assess the validity and acceptability of commercially available devices for monitoring step-count and activity time among people with MS. Nineteen ambulatory adults with MS [mean (SD) age 52.1 (11.9) years] participated in the study. Step-count was assessed using five commercially available devices (Fitbit Alta, Fitbit Zip, Garmin Vivofit 4, Yamax Digi Walker SW200, and Letscom monitor) and an activPAL3μ while completing nine everyday activities. Step-count was also manually counted. Time in light activity, moderate-to-vigorous activity, and total activity were measured during activities using an Actigraph GT3X accelerometer. Of the 19 participants who completed the validity study, fifteen of these people also wore the five commercially available devices for three consecutive days each, and participated in a semi-structured interview regarding their perception of the acceptability of the monitors. Mean percentage error for step-count ranged from 12.1% for the Yamax SW200 to −112.3% for the Letscom. Mean step-count as manually determined differed to mean step-count measured by the Fitbit Alta (p = 0.002), Garmin vivofit 4 (p < 0.001), Letscom (p < 0.001) and the research standard device, the activPAL3μ (p < 0.001). However, 95% limits of agreement were smallest for the activPAL3μ and largest for the Fitbit Alta. Median percentage error for activity minutes was 52.9% for the Letscom and 100% for the Garmin Vivofit 4 and Fitbit Alta compared to minutes in total activity. Three inductive themes were generated from participant accounts: Interaction with device; The way the device looks and feels; Functionality. In conclusion, commercially available devices demonstrated poor criterion validity when measuring step-count and activity time in people with MS. This negatively affected the acceptability of devices, with perceived inaccuracies causing distrust and frustration. Additional considerations when designing devices for people with MS include an appropriately sized and lit display and ease of attaching and charging devices.

Estimation of Hemi-Abdominal-Based Free Flap Weight Using Two Computed Tomography-Derived Measurements.

Accurate flap weight estimation is crucial for preoperative planning in microsurgical breast reconstruction; however, current flap weight estimation methods are time consuming. It was our objective to develop a parsimonious and accurate formula for the estimation of abdominal-based free flap weight. Patients who underwent hemi-abdominal-based free tissue transfer for breast reconstruction at a single institution were retrospectively reviewed. Subcutaneous tissue thicknesses were measured on axial computed tomography angiograms at several predetermined points. Multivariable linear regression was used to generate the parsimonious flap weight estimation model. Split-sample validation was used to for internal validation. A total of 132 patients (196 flaps) were analyzed, with a mean body mass index of 31.2 ± 4.0 kg/m2 (range: 22.6-40.7). The mean intraoperative flap weight was 990 ± 344 g (range: 368-2,808). The full predictive model (R 2 = 0.68) estimated flap weight using the Eq. 91.3x + 36.4y + 6.2z - 1030.0, where x is subcutaneous tissue thickness (cm) 5 cm lateral to midline at the level of the anterior superior iliac spine (ASIS), y is distance (cm) between the skin overlying each ASIS, and z is patient weight (kg). Two-thirds split-sample validation was performed using 131 flaps to build a model and the remaining 65 flaps for validation. Upon validation, we observed a median percent error of 10.2% (interquartile range [IQR]: 4.5-18.5) and a median absolute error of 108.6 g (IQR: 45.9-170.7). We developed and internally validated a simple and accurate formula for the preoperative estimation of hemi-abdominal-based free flap weight for breast reconstruction.

A wearable multi-sensor system for real world gait analysis.

Gait analysis is commonly performed in standardized environments, but there is a growing interest in assessing gait also in ecological conditions. In this regard, an important limitation is the lack of an accurate mobile gold standard for validating any wearable system, such as continuous monitoring devices mounted on the trunk or wrist. This study therefore deals with the development and validation of a new wearable multi-sensor-based system for digital gait assessment in free-living conditions. In particular, results obtained from five healthy subjects during lab-based and real-world experiments were presented and discussed. The in-lab validation, which assessed the accuracy and reliability of the proposed system, shows median percentage errors smaller than 2% in the estimation of spatio-temporal parameters. The system also proved to be easy to use, comfortable to wear and robust during the out-of-lab acquisitions, showing its feasibility for free-living applications.

Cardiac Output Measurement in Neonates and Children Using Noninvasive Electrical Bioimpedance Compared With Standard Methods: A Systematic Review and Meta-Analysis.

To systematically review and meta-analyze the validity of electrical bioimpedance-based noninvasive cardiac output monitoring in pediatrics compared with standard methods such as thermodilution and echocardiography. Systematic searches were conducted in MEDLINE and EMBASE (2000-2019). Method-comparison studies of transthoracic electrical velocimetry or whole body electrical bioimpedance versus standard cardiac output monitoring methods in children (0-18 yr old) were included. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. Mean differences of cardiac output, stroke volume, or cardiac index measurements were pooled using a random-effects model (R Core Team, R Foundation for Statistical Computing, Vienna, Austria, 2019). Bland-Altman statistics assessing agreement between devices and author conclusions about inferiority/noninferiority were extracted. Twenty-nine of 649 identified studies were included in the qualitative analysis, and 25 studies in the meta-analyses. No significant difference was found between means of cardiac output, stroke volume, and cardiac index measurements, except in exclusively neonatal/infant studies reporting stroke volume (mean difference, 1.00 mL; 95% CI, 0.23-1.77). Median percentage error in child/adolescent studies approached acceptability (percentage error less than or equal to 30%) for cardiac output in L/min (31%; range, 13-158%) and stroke volume in mL (26%; range, 14-27%), but not in neonatal/infant studies (45%; range, 29-53% and 45%; range, 28-70%, respectively). Twenty of 29 studies concluded that transthoracic electrical velocimetry/whole body electrical bioimpedance was noninferior. Transthoracic electrical velocimetry was considered inferior in six of nine studies with heterogeneous congenital heart disease populations. The meta-analyses demonstrated no significant difference between means of compared devices (except in neonatal stroke volume studies). The wide range of percentage error reported may be due to heterogeneity of study designs, devices, and populations included. Transthoracic electrical velocimetry/whole body electrical bioimpedance may be acceptable for use in child/adolescent populations, but validity in neonates and congenital heart disease patients remains uncertain. Larger studies in specific clinical contexts with standardized methodologies are required.

Precurved, Fiber-Reinforced Actuators Enable Pneumatically Efficient Replication of Complex Biological Motions.

Much of the research on bioinspired soft robotics has focused on capturing the interplay of biological form and function. However, existing soft robotic actuators are mostly made with linear or planar fabrication orientations that do not represent the resting geometry of complex biological systems, such as curved musculature. This work introduces the ability to create fiber-reinforced actuators with precurved configurations. By tuning variables such as dimensions and fiber angles, an optimization algorithm can prescribe the mechanical fabrication parameters to create a fiber-reinforced actuator that can generate controlled motion to follow a desired input trajectory. Precurved configurations introduce an additional optimization parameter, the initial bend angle, allowing for a more accurate and robust algorithm and generating a median percent error of <1%. With a customized software tool, we can take existing motion data from biological systems-such as medical imaging-and build soft robotic actuators optimized to replicate these trajectories. We can predict the motion of precurved actuators both analytically and numerically and replicate the motion experimentally, with excellent trajectory matching between the three. In constructing actuators that better match the native forms found within biological systems, we find that precurved actuators are more efficient than their initially straight counterparts. This pneumatic efficiency allows for the use of control systems with lower power and precision, lowering the economic cost of the associated control hardware, while more accurately replicating the biological motion. Taking two examples from biology, that of the human diaphragm during respiration and that of a jellyfish bell during locomotion, we design and generate fiber reinforced actuators to mimic these motions.