Trainable COSFIRE filters for vessel delineation with application to retinal images

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis.We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding.The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.     

A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images

Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi-resolution framework. The model parameters are then modified with the prior knowledge of the optimization space to achieve optimal prostate segmentation. In contrast to traditional statistical models of shape and intensity priors, we use posterior probabilities of the prostate region determined from random forest classification to build our appearance model, initialize and propagate our model. Furthermore, multiple mean models derived from spectral clustering of combined shape and appearance parameters are applied in parallel to improve segmentation accuracies. The proposed method achieves mean Dice similarity coefficient value of 0.91 ± 0.09 for 126 images containing 40 images from the apex, 40 images from the base and 46 images from central regions in a leave-one-patient-out validation framework. The mean segmentation time of the procedure is 0.67 ± 0.02 s.     

Fast and robust multi-atlas segmentation of brain magnetic resonance images

We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts. The segmentation pipeline is evaluated with two data cohorts: IBSR data (N = 18, six subcortial structures: thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data (N =   60, hippocampus). The average similarity index between automatically and manually generated volumes was 0.849 (IBSR, six subcortical structures) and 0.880 (ADNI, hippocampus). The correlation coefficient for hippocampal volumes was 0.95 with the ADNI data. The computation time using a standard multicore PC computer was about 3–4 min. Our results compare favourably with other recently published results.     

Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd–EOB–DTPA-enhanced MRI

Accurate detection of liver lesions is of great importance in hepatic surgery planning. Recent studies have shown that the detection rate of liver lesions is significantly higher in gadoxetic acid-enhanced magnetic resonance imaging (Gd–EOB–DTPA-enhanced MRI) than in contrast-enhanced portal-phase computed tomography (CT); however, the latter remains essential because of its high specificity, good performance in estimating liver volumes and better vessel visibility. To characterize liver lesions using both the above image modalities, we propose a multimodal nonrigid registration framework using organ-focused mutual information (OF-MI). This proposal tries to improve mutual information (MI) based registration by adding spatial information, benefiting from the availability of expert liver segmentation in clinical protocols. The incorporation of an additional information channel containing liver segmentation information was studied. A dataset of real clinical images and simulated images was used in the validation process. A Gd–EOB–DTPA-enhanced MRI simulation framework is presented. To evaluate results, warping index errors were calculated for the simulated data, and landmark-based and surface-based errors were calculated for the real data. An improvement of the registration accuracy for OF-MI as compared with MI was found for both simulated and real datasets. Statistical significance of the difference was tested and confirmed in the simulated dataset (p < 0.01).     

A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo

The development of sophisticated and high throughput whole body small animal imaging technologies has created a need for improved image analysis and increased automation. The registration of a digital mouse atlas to individual images is a prerequisite for automated organ segmentation and uptake quantification. This paper presents a fully-automatic method for registering a statistical mouse atlas with individual subjects based on an anterior–posterior X-ray projection and a lateral optical photo of the mouse silhouette. The mouse atlas was trained as a statistical shape model based on 83 organ-segmented micro-CT images. For registration, a hierarchical approach is applied which first registers high contrast organs, and then estimates low contrast organs based on the registered high contrast organs. To register the high contrast organs, a 2D-registration-back-projection strategy is used that deforms the 3D atlas based on the 2D registrations of the atlas projections. For validation, this method was evaluated using 55 subjects of preclinical mouse studies. The results showed that this method can compensate for moderate variations of animal postures and organ anatomy. Two different metrics, the Dice coefficient and the average surface distance, were used to assess the registration accuracy of major organs. The Dice coefficients vary from 0.31 ± 0.16 for the spleen to 0.88 ± 0.03 for the whole body, and the average surface distance varies from 0.54 ± 0.06 mm for the lungs to 0.85 ± 0.10 mm for the skin. The method was compared with a direct 3D deformation optimization (without 2D-registration-back-projection) and a single-subject atlas registration (instead of using the statistical atlas). The comparison revealed that the 2D-registration-back-projection strategy significantly improved the registration accuracy, and the use of the statistical mouse atlas led to more plausible organ shapes than the single-subject atlas. This method was also tested with shoulder xenograft tumor-bearing mice, and the results showed that the registration accuracy of most organs was not significantly affected by the presence of shoulder tumors, except for the lungs and the spleen.     

Comparison of atlas-based techniques for whole-body bone segmentation

We evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean square distance (MSD) as image similarity measures for calculating the weighting factors, along with other atlas-dependent algorithms, such as (v) shape-based averaging (SBA) and (vi) Hofmann's pseudo-CT generation method. The performance evaluation of the different segmentation techniques was carried out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice criterion, global weighting atlas fusion methods provided moderate improvement of whole-body bone segmentation (DSC = 0.65 ± 0.05) compared to non-weighted IA (DSC = 0.60 ± 0.02). The local weighed atlas fusion approach using the MSD similarity measure outperformed the other strategies by achieving a DSC of 0.81 ± 0.03 while using the NCC and NMI measures resulted in a DSC of 0.78 ± 0.05 and 0.75 ± 0.04, respectively. Despite very long computation time, the extracted bone obtained from both SBA (DSC = 0.56 ± 0.05) and Hofmann's methods (DSC = 0.60 ± 0.02) exhibited no improvement compared to non-weighted IA. Finding the optimum parameters for implementation of the atlas fusion approach, such as weighting factors and image similarity patch size, have great impact on the performance of atlas-based segmentation approaches. The voxel-wise atlas fusion approach exhibited excellent performance in terms of cancelling out the non-systematic registration errors leading to accurate and reliable segmentation results. Denoising and normalization of MR images together with optimization of the involved parameters play a key role in improving bone extraction accuracy.     

Evaluating a medical directive for nurse-initiated analgesia in the Emergency Department

ObjectivesAlthough acute pain is a common presentation in the Emergency Department (ED), analgesics are often delayed until the patient is seen by a physician. We assessed the effect of a medical directive for nurse-initiated analgesia on time to first dose of analgesics, proportion of patients receiving analgesics in less than 30 min, and total length of stay in the ED.MethodsA medical directive for nurse-initiated analgesia was introduced in our ED in October 2011. This before-after health record review included all patients presenting to the ED with musculoskeletal back pain in 4 month periods before and after implementation of the medical directive.ResultsA total of 524 cases were reviewed, of which 401 were included – 201 and 200 in the before and after implementation groups respectively. After implementation there was a shorter time to first dose of analgesic (mean of 118 vs 160 min, p < 0.001), and a higher proportion of patients receiving analgesics in the first 30 min (20% vs 4%, p < 0.001). However there was no difference in total proportion of patients receiving analgesics (71% vs 67%, p = 0.46) or total length of stay in the ED (337 vs 323 min, p = 0.51).ConclusionsA medical directive for nurse-initiated analgesia in the ED was associated with significantly reduced time to the first dose of analgesic, and increased the proportion of patients receiving analgesics within 30 min. We can conclude that medical directives for nurse-initiated analgesia effectively improve the timeliness and quality of care for patients with acute pain.     

Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression

Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, p < 0.001). This method also showed statistically significantly improved recall rate compared to a 2-cue baseline method using fewer vessel cues (30.7% versus 67.7%, p < 0.001). These results demonstrate that marginal artery localization on CTC is feasible by combining a discriminative classifier (i.e., random forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse.     

Strategies to support transitions from hospital to home for children with medical complexity: A scoping review

BackgroundChildren with medical complexity constitute a small but resource-intensive subgroup of children with special health care needs. Their medical fragility and resource-intensive needs put them at greater risk for inadequate transitions from hospital to home-based care, and subsequent adverse outcomes and hospital re-admissions.ObjectiveThis scoping literature review was conducted to map empirically researched interventions, frameworks, programs or models that could inform or support the transition from hospital to home for children with medical complexity.DesignWe conducted a scoping review using the methodology outlined by the Joanna Briggs Institute.Data sourcesIn consultation with an experienced librarian, we searched PubMed, EMBASE and CINAHL for English-language articles published from the date of origin to February 2016. We also hand-searched four high impact journals and searched the reference lists of relevant articles.Review methodsTwo reviewers independently screened the literature results according to inclusion criteria. Empirically designed studies that targeted children <18 years old who were specifically defined as medically complex or fragile and transitioning from acute care to home were included. Data were extracted using a predefined tool. Quality appraisal of the articles was conducted using the mixed methods appraisal tool (MMAT). Thematic analysis was carried out to identify existing patterns or trends in the included studies.ResultsOf the 2088 abstracts retrieved, 14 studies met the inclusion criteria. Following analysis, we identified three major categories of interventions: Comprehensive care plans (n = 3), Complex Care Programs (n = 8) and Integrated delivery models (n = 3). The overall quality of included studies was moderate, with 21% (n = 3) scoring 0.25, 29% (n = 4) scoring 0.50, 43% (n = 6) scoring 0.75, and 7% (n = 1) scoring 1.0.ConclusionsIn the absence of evidence-based guidelines to ensure adequate transitions from hospital to home for children with medical complexity, identification of potential models to support this transition is imperative. We identified interventions, frameworks, models and programs in the literature that might inform the development of such guidelines; however, there is a need for consensus around the definition for children with medical complexity and the limited number of these studies and lack of high quality of evidence signals the need for further research to improve the transition from hospital to home and ultimately, improve patient and family outcomes.     

Respiratory motion compensation by model-based catheter tracking during EP procedures

In many cases, radio-frequency catheter ablation of the pulmonary veins attached to the left atrium still involves fluoroscopic image guidance. Two-dimensional X-ray navigation may also take advantage of overlay images derived from static pre-operative 3D volumetric data to add anatomical details otherwise not visible under X-ray. Unfortunately, respiratory motion may impair the utility of static overlay images for catheter navigation. We developed a novel approach for image-based 3D motion estimation and compensation as a solution to this problem. It is based on 3D catheter tracking which, in turn, relies on 2D/3D registration. To this end, a bi-plane C-arm system is used to take X-ray images of a special circumferential mapping catheter from two directions. In the first step of the method, a 3D model of the device is reconstructed. Three-dimensional respiratory motion at the site of ablation is then estimated by tracking the reconstructed catheter model in 3D based on bi-plane fluoroscopy. Phantom data and clinical data were used to assess model-based catheter tracking. Our phantom experiments yielded an average 2D tracking error of 1.4 mm and an average 3D tracking error of 1.1 mm. Our evaluation of clinical data sets comprised 469 bi-plane fluoroscopy frames (938 monoplane fluoroscopy frames). We observed an average 2D tracking error of 1.0 ± 0.4 mm and an average 3D tracking error of 0.8 ± 0.5 mm. These results demonstrate that model-based motion-compensation based on 2D/3D registration is both feasible and accurate.     

Impacts of the introduction of a triage system in Japan: A time series study

The aim of the present study was to measure and compare the effectiveness of nursing triage before and after introduction of the Japanese Triage and Acuity Scale (JTAS), the Japanese version of the Canadian Triage and Acuity Scale (CTAS), during emergency treatment. Surveys of triage nurses and emergency physicians were conducted before and after JTAS introduction. Respondents were triage nurses (before 112 cases, after 94 cases), emergency physicians (before 50, after 41), and triaged patients (before 1057, after 1025) from seven separate emergency medical facilities. The results showed that nursing triage using the JTAS shortened “time from registration to triage” by 3.8 min, “triage duration” by 1 min, “time from registration to physician” by 11.2 min, and “waiting time perceived by patients to see a physician” by 18.6 min (p < 0.001). The difference in assigned level of urgency between triage nurses and emergency physicians decreased from 34.2% to 12.2% (p < 0.001), over-triage decreased from 24.7% to 8.6% (p < 0.001), and under-triage decreased from 9.5% to 3.6% (p < 0.001). Furthermore, assessment agreement between triage nurses and emergency physicians increased significantly, from weighted κ = 0.486 to weighted κ = 0.820. These findings suggest that the introduction of the JTAS promoted more effective nursing triage and medical care.     

Upper torso pain and musculoskeletal structure and function in women with and without large breasts: A cross sectional study

BackgroundWomen with large breasts frequently experience upper torso pain secondary to their breast size. Evidence is lacking on the underlying causes of this pain. This study investigated whether upper torso pain and musculoskeletal structure and function differed between women with large breasts and women with small breasts.MethodsA linear regression, adjusting for body mass, compared the upper torso pain, thoracic flexion torque due to breast mass, thoracic kyphosis, shoulder active range-of-motion, and scapular retraction muscle strength of 27 women with large breasts (bilateral breast volume > 1200 ml, age 45.9 y SD 9.9 y, BMI 29.0 kg/m² SD 3.8 kg/m²) and 26 women with small breasts (bilateral breast volume < 800 ml, age 43.8 y SD10.9 y, BMI 23.3 kg/m² SD 2.9 kg/m²).FindingsWomen with large breasts reported a higher upper torso pain score (46.6, 95%CI 33.3–58.0 versus 24.1, 95%CI 12.5–37.8), accompanied by a larger flexion torque (5.9 Nm, 95%CI 4.5–5.8 Nm versus 0.9 Nm, 95%CI 0.8–2.4 Nm), greater thoracic kyphosis (34°, 95%CI 31–38° versus 27°, 95% CI 24–31°), decreased shoulder elevation range-of-motion (160°, 95%CI 158–163° versus 169°, 95%CI 166–172°), and decreased scapular retraction endurance-strength (511.4 s, 95%CI 362.2–691.3 s versus 875.8 s, 95%CI 691.5–1028.4 s) compared to the women with small breasts.InterpretationDifferences in the upper torso posture, range-of-motion, and muscle strength of women with large breasts provides insight into underlying causes of their musculoskeletal pain. This information can be used to develop evidence-based assessment and treatment strategies to relieve and prevent symptom progression.     

Fast and robust 3D ultrasound registration – Block and game theoretic matching

Real-time 3D US has potential for image guidance in minimally invasive liver interventions. However, motion caused by patient breathing makes it hard to visualize a localized area, and to maintain alignment with pre-operative information. In this work we develop a fast affine registration framework to compensate in real-time for liver motion/displacement due to breathing. The affine registration of two consecutive ultrasound volumes in time is performed using block-matching. For a set of evenly distributed points in one volume and their correspondences in the other volume, we propose a robust outlier rejection method to reject false matches. The inliers are then used to determine the affine transformation. The approach is evaluated on 13 4D ultrasound sequences acquired from 8 subjects. For 91 pairs of 3D ultrasound volumes selected from these sequences, a mean registration error of 1.8 mm is achieved. A graphics processing unit (GPU) implementation runs the 3D US registration at 8 Hz.     

Statistical coronary motion models for 2D + t/3D registration of X-ray coronary angiography and CTA

Accurate alignment of intra-operative X-ray coronary angiography (XA) and pre-operative cardiac CT angiography (CTA) may improve procedural success rates of minimally invasive coronary interventions for patients with chronic total occlusions. It was previously shown that incorporating patient specific coronary motion extracted from 4D CTA increases the robustness of the alignment. However, pre-operative CTA is often acquired with gating at end-diastole, in which case patient specific motion is not available.For such cases, we investigate the possibility of using population based coronary motion models to provide constraints for the 2D + t/3D registration. We propose a methodology for building statistical motion models of the coronary arteries from a training population of 4D CTA datasets. We compare the 2D + t/3D registration performance of the proposed statistical models with other motion estimates, including the patient specific motion extracted from 4D CTA, the mean motion of a population, the predicted motion based on the cardiac shape.The coronary motion models, constructed on a training set of 150 patients, had a generalization accuracy of 1 mm root mean square point-to-point distance. Their 2D + t/3D registration accuracy on one cardiac cycle of 12 monoplane XA sequences was similar to, if not better than, the 4D CTA based motion, irrespective of which respiratory model and which feature based 2D/3D distance metric was used. The resulting model based coronary motion estimate showed good applicability for registration of a subsequent cardiac cycle.     

Single-image super-resolution of brain MR images using overcomplete dictionaries

Resolution in Magnetic Resonance (MR) is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. This paper presents a sparse-based super-resolution method, adapted for easily including prior knowledge, which couples up high and low frequency information so that a high-resolution version of a low-resolution brain MR image is generated. The proposed approach includes a whole-image multi-scale edge analysis and a dimensionality reduction scheme, which results in a remarkable improvement of the computational speed and accuracy, taking nearly 26 min to generate a complete 3D high-resolution reconstruction. The method was validated by comparing interpolated and reconstructed versions of 29 MR brain volumes with the original images, acquired in a 3T scanner, obtaining a reduction of 70% in the root mean squared error, an increment of 10.3 dB in the peak signal-to-noise ratio, and an agreement of 85% in the binary gray matter segmentations. The proposed method is shown to outperform a recent state-of-the-art algorithm, suggesting a substantial impact in voxel-based morphometry studies.     

Physiotherapy screening of patients referred for orthopaedic consultation in primary healthcare – A randomised controlled trial

A large proportion of patients who consult primary healthcare for musculoskeletal pain are referred for orthopaedic consultation, but only a small number of these patients are appropriate for orthopaedic intervention. Experienced physiotherapists have the appropriate knowledge to manage musculoskeletal disorders. The primary aim of this randomised study was therefore to evaluate a screening by a physiotherapist of patients referred for orthopaedic consultation compared to standard practice in primary care.Patients referred for orthopaedic consultation (n = 203) were randomised to physiotherapy screening or standard practice. Selection accuracy for orthopaedic intervention and other referrals were analysed with proportion analysis. Patient views of the quality of care were analysed with Mann–Whitney U-test, waiting time with Independent t-test.There was higher selection accuracy for orthopaedic intervention in the physiotherapy screening group (p = 0.002). A smaller proportion of patients in the screening group were referred back to their general practitioner (GP) (p < 0.001) and a larger proportion to the physiotherapy clinic (p < 0.001) compared to standard practice. The proportion of patients referred for further investigations was significantly lower in the physiotherapy screening group (p < 0.039). Waiting time was shorter in the screening group (p < 0.001). A large proportion of the patients reported no hesitation to attend the clinic for future care, no difference between the groups (p < 0.95).The findings in this study suggest that an experienced physiotherapist effectively can screen patients referred for orthopaedic consultation in primary healthcare.     

Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology

In the last 20 years, 3D angiographic imaging has proven its usefulness in the context of various clinical applications. However, angiographic images are generally difficult to analyse due to their size and the complexity of the data that they represent, as well as the fact that useful information is easily corrupted by noise and artifacts. Therefore, there is an ongoing necessity to provide tools facilitating their visualisation and analysis, while vessel segmentation from such images remains a challenging task. This article presents new vessel segmentation and filtering techniques, relying on recent advances in mathematical morphology. In particular, methodological results related to spatially variant mathematical morphology and connected filtering are stated, and included in an angiographic data processing framework. These filtering and segmentation methods are evaluated on real and synthetic 3D angiographic data.     

Automatic carotid artery distensibility measurements from CTA using nonrigid registration

The distensibility of a blood vessel is a marker of atherosclerotic disease. In this paper we investigate the feasibility of measuring carotid artery distensibility on 4D CTA, both manually and using a new automatic method. On 4D CTA datasets manual (n = 38) and automatic (n = 76) measurements of the carotid distensibility were performed. A subset (n = 10) of the manual annotations were repeated by a second observer. The interobserver variability was assessed using a Bland–Altman analysis and appeared to be too large to reliably measure the distensibility using manual annotation. We compared two versions of the automatic method: one using 3D registration and one using a 4D registration method. The latter resulted in a more smooth deformation over time. The automatic method was evaluated using a synthetic deformation and by investigating whether known relations with cardiovascular risk factors could be reproduced. The relation between distensibility and cardiovascular risk factors was tested with a Mann–Whitney U test. Automatic measurements revealed an association with hypertension whereas the manual measurements did not. This relation has been found by other studies too. We conclude that carotid artery distensibility measurements should be performed automatically and that the method described in this paper is suitable for that. All CTA datasets and related clinical data used in this study can be downloaded from our website (http://ctadist.bigr.nl).     

Complexity of knee extensor torque in patients with frailty syndrome: a cross-sectional study

BackgroundFrailty syndrome is characterized by a marked reduction in physiological reserves and a clinical state of vulnerability to stress. Torque complexity analysis could reveal changes in the musculoskeletal systems that are the result of having the syndrome.ObjectiveThe aim of this study was to evaluate the complexity of submaximal isometric knee extensor torque in frail, pre-frail, and non-frail older adults. A secondary aim was to analyze the torque complexity behavior in different force levels in each group.MethodsA cross-sectional study was conducted. Forty-two older adults were divided into three groups: non-frail (n = 15), pre-frail (n = 15), and frail (n = 12). The data collected included body composition, five times sit-to-stand test, walking speed, and isometric knee extensor torque at 15, 30, and 40% of maximal voluntary contraction. The knee extensor torque variability was evaluated by coefficient of variation, and the torque complexity was evaluated by approximate entropy and sample entropy.ResultsThe frail group presented a reduction in body mass and peak torque value compared to the non-frail group. Also, the frail group showed worse physical performance (on the five times sit-to-stand test and walking speed) compared to the pre-frail and non-frail groups. In addition, the frail older adults showed reduced torque complexity compared to the non-frail group. Finally, the association between torque complexity and force levels remained similar in all groups.ConclusionTorque complexity is reduced in the presence of frailty syndrome.