scholarly journals Evaluation of a Weightbearing CT Artificial Intelligence-based Automatic Measurement for Hallux Valgus: A Case-Control Study

2020 ◽  
Vol 5 (4) ◽  
pp. 2473011420S0003
Author(s):  
Jonathan Day ◽  
Francois Lintz ◽  
Martinus Richter ◽  
Céline Fernando ◽  
Scott J. Ellis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Hallux Valgus ◽  
Case Control Study ◽  
Weight Bearing ◽  
Intraclass Correlation ◽  
Automatic Measurement ◽  
Case Control ◽  
Foot And Ankle ◽  
Beta Version ◽  
Control Study

Category: Bunion; Other Introduction/Purpose: Cone Beam Weight Bearing CT (WBCT) is gaining traction, particularly in the foot and ankle, due to the ability to perform natural stance weight bearing 3D scans. However, the resulting wealth of 3D data renders daily clinical use time consuming. Therefore, reliable automatic measurements are indispensable in order to make best use of the technology. The aim of this study was to evaluate a beta-version WBCT artificial intelligence (AI) automatic measurement system for the M1-M2 intermetatarsal angle (IMA), which is applicable in the absence of metallic hardware in the foot and ankle. We hypothesized that automatic measurements would correlate well with human measurements, and that software reproducibility would be better and close to perfect compared to manual measurements. Methods: In this retrospective case-control study, 90 feet were included from patients who underwent WBCT scans during routine follow up: 44 feet (90.9% female, mean age 54 years) with symptomatic hallux valgus (HV), 46 controls (76.1% female, mean age 49 years). Patients were excluded if they had history of surgery or trauma involving the first or second metatarsals, hallux rigidus, or presence of metal in their foot/ankle. IMA was measured manually on Digitally Reconstructed Radiographs (DRR IMA) and automatically with AI software producing auto 2D (ground plane projection) and 3D (multiplanar) measurements. Each IMA DRR was measured by two independent raters twice to calculate intraclass correlation coefficients (ICCs). To assess intra- software reliability, AI software measurements were made twice on each dataset. Manual and automatic measurements were compared between HV and control groups. Failures of the AI software to produce a measurement were recorded. Results: Mean values for controls were 8.6° +-1.8° (range, 5°-14°) for the manually measured DRR IMA, 9.3° +-2.8° (range, 3°- 17°) for auto 2D, and 9.2° +-2.6° (range, 3°-16°) for auto 3D IMA measurements. Compared to controls, HV patients demonstrated significantly increased IMA (p<0.0001): 14.2° +-2.7° (range, 8°-21°) for the manually measured DRR IMA, 15.4°+- 4.4° (range, 8°-26°) for auto 2D, and 15.1° +-4.1° (range 8°-28°) for auto 3D IMA measurements. There were strong correlations (r=0.75 and r=0.80) between manual and auto 2D and 3D measurements. Intraobserver and interobserver ICCs for DRR IMA were 0.96 and 0.90, respectively, and the intra-software ICCs for the AI were near 1.0 for both auto 2D and auto 3D IMA. The AI software failed in 32.3% cases. Conclusion: Our results demonstrated strong correlation between a WBCT Artificial Intelligence based automatic measurement for IMA with human measurements, with the ability to distinguish HV from control with close to 100% repeatability. However, the number of failures was still high due to the early stage beta-version of the algorithm tested. While these early results are promising, further developments are warranted in order to improve usability of this tool in daily practice, especially in the presence of metal hardware. [Figure: see text]

scholarly journals Hallux valgus measurements using weight-bearing computed tomography: what changes?

2021 ◽  
Vol 15 (3) ◽  
pp. 259-264
Author(s):  
Samuel Braza ◽  
Nacime Salomão Barbachan Mansur ◽  
Vineel Mallavarapu ◽  
Kepler Alencar Mendes de Carvalho ◽  
Kevin Dibbern ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Hallux Valgus ◽  
Case Control Study ◽  
Weight Bearing ◽  
Metatarsal Head ◽  
Case Control ◽  
Retrospective Case ◽  
Level Of Evidence ◽  
Mean Values ◽  
Control Study

Objective: To assess whether traditional hallux valgus (HV) measurements obtained with conventional radiography (CR) correspond to those obtained with weight-bearing computed tomography (WBCT). Methods: In this retrospective case-control study, 26 HV feet and 20 control feet were analyzed with CR and WBCT. Hallux valgus angle (HVA), intermetatarsal angle (IMA), interphalangeal angle (IPA), distal metatarsal articular angle (DMAA), sesamoid station (SS), and first metatarsal head shape were measured. Chi-square tests were used to compare hallux valgus and control patients. T-tests were used to compare CR and WBCT. P-values less than 0.05 were considered significant. Results: WBCT was capable of discriminating patients with HV from controls, showing higher mean values for HV patients than controls in HVA (35.29 and 9.02, p < 0.001), IMA (16.01 and 10.01, p < 0.001), and DMAA (18.90 and 4.10, p < 0.001). When comparing the two methods, differences were not significant between CR and WBCT measurements in HVA (-0.84, p = 0.79), IMA (-0.93, p = 0.39), IPA (1.53, p = 0.09), or SS (p = 0.40), but were significant for DMAA (13.43, p < .0001). CR analysis yielded varied metatarsal head shapes, while all WBCT shape classifications were round.  Conclusion: Unidimensional HV measurements were similar between WBCT and CR, while more three-dimensional findings were not. CR may be used to assess the axial aspects of HV, but multidimensional aspects of the deformity may not be accurately assessed with plain radiographs. Level of Evidence III; Therapeutic Studies; Retrospective Case-Control Study.

scholarly journals Three-Dimensional Weightbearing Assessment of the First Ray in Hallux Valgus: A Case-Control Study

2019 ◽  
Vol 4 (4) ◽  
pp. 2473011419S0005
Author(s):  
Francois Lintz ◽  
Alessio Bernasconi ◽  
Celine Fernando ◽  
Matthew Welck ◽  
Cesar de Cesar Netto
Keyword(s):  
Hallux Valgus ◽  
Case Control Study ◽  
Weight Bearing ◽  
Three Dimensional ◽  
Case Control ◽  
Rotational Deformity ◽  
Intermetatarsal Angle ◽  
First Ray ◽  
3D Architecture ◽  
Control Study

Category: Bunion Introduction/Purpose: Coronal plane rotational deformity of the first ray has been implicated with the developmental pathologic process of hallux valgus (HV). Weight Bearing CT (WBCT) is an imaging technology that can support the assessment of this complex three-dimensional (3D) deformity. The objective of the study was to analyze the 3D architecture of the first ray in patients with HV when compared to controls using WBCT images and a 3D biometric algorithm analyzing the deformity in all three planes. We hypothesized that WBCT would confirm the rotational deformity in HV patients, and that the 3D algorithm would demonstrate increased specificity and sensitivity for the pathology when compared to traditional two-dimensional (2D) HV measurements such as the 1-2 intermetatarsal angle. Methods: Retrospective case-control study, ethics committee approved. Twenty-one feet of patients with clinically symptomatic HV and 20 feet of asymptomatic controls were included. Exclusion criteria applied were previous trauma or surgery affecting first ray or forefoot morphology. All patients were assessed using WBCT. First ray 3D coordinates (x, y, z) were harvested including: center-points of the heads and bases of the first and second metatarsals, center-point of the medial and lateral sesamoids, distal condyles of the proximal phalanx (PP) of the first toe, as well as the medial and lateral borders of the first metatarsal head and diaphysis. The 2D measurements (dorsoplantar 1-2 intermetatarsal (IMA) and metatarsophalangeal (MPA) angles) were obtained using digitally reconstructed radiographs (DRR). The Sesamoid Rotation Angle (SRA) was measured in the coronal plane. Using these coordinates, all 2D, 3D axes, distances, angulations and 3D biometric for HV (HV-3DB) could be calculated. Results: Mean ages were respectively 62.2y in the HV group and 48.8y in the control group (p<0.05). In 2D, the mean IMA and MPA for HV/controls were respectively 14.9/9.3 (p<0.001) and 30.1/13.1 (p<0.001). The SRA were respectively 29.1/7.1 (p<0.001). We found an almost perfect positive correlation between P1 rotation and sesamoid rotation, good correlation between IMA, MPA and SRA angles. There was poor correlation between pronation angles of the 1st phalanx and the 1st metatarsal. The 3D biometric algorithm combining IMA, MPA and SRA had a sensitivity of 95% and a specificity of 95.2% for the diagnosis of HV, compared to 90%/85.6% for the IMA and 90%/90.5% for the SRA. Conclusion: This original study confirmed our hypotheses. Weight Bearing CT efficiently analyzed the 3D architecture of the 1st ray in HV patients compared to asymptomatic controls. We concur with previous findings described in the literature concerning increased pronation of the 1st ray in HV. A novel biometric for HV using a specific multidimensional algorithm which combined IMA, HVA and SRA in a single 3D measurement, demonstrated increased sensitivity and specificity compared to the conventional 2D 1-2 intermetatarsal angle for the diagnosis of HV.

scholarly journals Clinical Significance of Measurements in Adult Acquired Flatfoot Deformity using Weight Bearing CT: A Matched Case-Control Study

2020 ◽  
Vol 5 (4) ◽  
pp. 2473011420S0033
Author(s):  
Francois Lintz ◽  
Alessio Bernasconi ◽  
Matthieu Lalevée ◽  
Céline Fernando ◽  
Alexej Barg ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Case Control Study ◽  
Weight Bearing ◽  
Roc Curves ◽  
Case Control ◽  
Foot And Ankle ◽  
Adult Acquired Flatfoot Deformity ◽  
Diagnostic Power ◽  
2D And 3D ◽  
Control Study

Category: Hindfoot; Other Introduction/Purpose: Adult Acquired Flatfoot Deformity (AAFD) results in progressive foot collapse through peritalar subluxation. Numerous radiographic and Weight Bearing CT (WBCT) measurements have been described in the literature aiming to gauge the severity of the multiple components of the deformity. However, the real diagnostic power of each measurement is currently unknown. Moreover, novel measurements have recently been described such as 3D biometrics and multidimensional measurements. The objective of this case-control study was to individually assess the diagnostic accuracy of known 2D and 3D WBCT measurements and to compare it with a novel multidimensional measurement. We hypothesized that the latter would demonstrate superior diagnostic power than isolated 2D and 3D measurements. Methods: Retrospective case-control study, including 19 AAFD feet and 19 controls that were matched for age, gender and BMI (9 male, 10 female, mean age 54.4 years in both groups). All patients had standing WBCT imaging as baseline assessment of their foot pathology. 2D measurements assessed included: axial and sagittal talus-first metatarsal angles (TM1A), talonavicular coverage angle (TNCA), forefoot arch angle (FFAA) and middle facet incongruence angle (MF°) and uncoverage percentage (MF%). The 3D Foot and Ankle Offset (FAO) was calculated using semi-automatic software. A novel multiplanar biometric measurement (AAFD- MD) was calculated using a multidimensional mathematical algorithm that pooled multiplanar 2D measurements. Intra and interobserver reliabilities were assessed. Comparisons between variables were done using Student-t test or Wilcoxon rank-sum test. Receiver Operating Characteristic (ROC) curves were calculated to determine diagnostic accuracy, sensitivity and specificity of each measurement. Results: AUC for ROC curves were 1. for MF%, 0.96 for FAO, 0.94 for MF° and 0.92 for AAFD-MD. For MF%, a threshold value equal of greater than 28.1% was found to be diagnostic of AAFD with a sensitivity of 100% and specificity of 100%. FFAA were decreased in AAFD: 6.3° versus 15.2° in controls (p<0.001). Axial and sagittal TM1A were respectively 17.6° and 20.8° in AAFD, while in controls: 7.5° (p<0.001) and 6.3° (p< 0.001). The TNCA was increased in AAFD: 27.9° versus 15.6° in controls (p<0.001). In AAFD, MF° and MF% were respectively 13° and 49.4% compared with 5.3° and 10.6% in controls (p<0.001 for both). The FAO was 7.5% in AAFD and 1.1% in controls (p<0.001). Conclusion: The observed results did not confirm our hypothesis. The multidimensional measurement was not as accurate a diagnostic tool as Middle Facet uncoverage percentage which expresses the amount of subluxation of the MF. In that respect, this could mean that congruency of the middle facet could be the last frontier between asymptomatic Pes Planovalgus and symptomatic AAFD, leading to progressive foot collapse, secondarily affecting the FAO. These results also give insight into the meaning of the FAO, which appears here to be a more general assessment of the Foot and Ankle Complex alignment, rather than a marker for a specific pathology.

scholarly journals Middle Subtalar Joint Facet Subluxation as a Marker of Collapsing Flatfoot Deformity: A Case-Control Study

2019 ◽  
Vol 4 (4) ◽  
pp. 2473011419S0015
Author(s):  
Cesar de Cesar Netto ◽  
Alexandre Leme Godoy-Santos ◽  
Lauren Roberts ◽  
Guilherme Honda Saito ◽  
Francois Lintz ◽  
...  
Keyword(s):  
Subtalar Joint ◽  
Case Control Study ◽  
Progressive Collapse ◽  
Interobserver Reliability ◽  
Intraclass Correlation ◽  
Case Control ◽  
Mean Value ◽  
Longitudinal Length ◽  
Joint Incongruence ◽  
Control Study

Category: Hindfoot Introduction/Purpose: Peritalar subluxation comprises part of the three-dimensional and complex distortion that occurs in patients with adult-acquired flatfoot deformity (AAFD) and is characterized by subluxation of the hindfoot through the triple joint complex. It is traditionally graded on weightbearing computed tomography (WBCT) coronal plane images, depending upon the degree of angulation and subluxation of the posterior facet of the subtalar joint. In this case-control study, we describe a new marker of peritalar subluxation represented by the amount of subluxation and joint incongruence of the middle facet of the subtalar joint. We hypothesized that the amount of joint subluxation and incongruence at the middle facet would be significantly increased in patients with AAFD when compared to controls. Methods: Case-control study, we included 30 patients with stage II AAFD (19 females/11 males), mean age 52.2 (range, 29 to 81) years, and 30 controls (18 females/12 males), mean age 49.3 (range, 28 to 83) years, that underwent WBCT as part of the evaluation of their foot condition. Age and gender were statistically similar in both groups. The amount of subluxation of the subtalar joint at the middle facet (% of uncoverage) and angle of joint incongruence, both measured at the midpoint of its longitudinal length, was measured in coronal WB CBCT images by two independent and blinded fellowship-trained foot and ankle surgeons. A second set of measurements was performed after one month (wash-out period). Intra- and interobserver reliability were assessed by Pearson/Spearman’s and Intraclass Correlation Coefficient (ICC), respectively. Comparison was performed using Paired Student T-Test or each pair Wilcoxon rank sum test. P-values lower than 0.05 were considered significant. Results: We found overall good to excellent intra- (range, 0.90-0.95) and interobserver reliability (range, 0.75-0.93) for the measurements. We found significantly increased subluxation of the subtalar joint at the middle facet in patients with AAFD, with a mean value for middle facet uncoverage of 45.3% (95% CI, 40.5% to 50.1%), when compared to 4.8% (95% CI, 0% to 9.6%) in controls (p<0.0001). Significant differences were also found for middle facet subtalar joint incongruence angle, with a mean value of 17.3 degrees (95% CI, 15.5 to 19.1) in AAFD patients and 0.3 degrees (95% CI, -1.5 to 2.1) in controls (p<0.0001). Conclusion: We described the use of the subtalar joint middle facet as a marker for peritalar subluxation in patients with AAFD. We found significant and marked differences in the percentage of joint uncoverage and incongruence when compared to controls. Hopefully, the use of the middle facet as an indicator for peritalar subluxation can potentially help in the early detection of high- risk AAFD patients for progressive collapse and development of sinus tarsi and subfibular impingement, as well as arthritic degeneration of the subtalar joint. Future longitudinal and prospective studies are needed.

Postural stability impairment in patients with bilateral hallux valgus: A case-control study using a stabilometer

2020 ◽  
Author(s):  
Hiroaki Shima ◽  
Toshito Yasuda ◽  
Takashi Hida ◽  
Seiya Tsujinaka ◽  
Kosho Togei ◽  
...  
Keyword(s):  
Hallux Valgus ◽  
Postural Stability ◽  
Case Control Study ◽  
Case Control ◽  
Control Study

Risk Factors Associated With Nonunion After Elective Foot and Ankle Reconstruction: A Case-Control Study

2017 ◽  
Vol 56 (3) ◽  
pp. 457-462 ◽  
Author(s):  
Kyle R. Moore ◽  
Michael A. Howell ◽  
Karl R. Saltrick ◽  
Alan R. Catanzariti
Keyword(s):  
Risk Factors ◽  
Case Control Study ◽  
Case Control ◽  
Foot And Ankle ◽  
Factors Associated ◽  
Control Study

scholarly journals Artificial Intelligence-Assisted Reduction in Patients’ Waiting Time for Outpatient Procedures: A Matched Case–Control Study

2020 ◽  
Author(s):  
Xiaoqing Li ◽  
Dan Tian ◽  
Weihua Li ◽  
Bin Dong ◽  
Hansong Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Laboratory Test ◽  
Waiting Time ◽  
Case Control Study ◽  
Statistical Significance ◽  
Case Control ◽  
Conventional Group ◽  
Imaging Examination ◽  
Outpatient Procedures ◽  
Control Study

Abstract Background: Many studies indicate that patient satisfaction is significantly negatively correlated with waiting time. A well-designed healthcare system should not keep patients waiting too long for appointment and consultation. However, in China, patients spend considerable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less.Methods: We developed an artificial intelligence (AI)-assisted module that is embedded in hospital information systems. Through its use, outpatients were automatically recommended an imaging examination or a laboratory test based on their symptoms and chief complaint. Thus, they could get examined or tested before they went to see the doctor. People who saw a doctor in the traditional way were assigned to the conventional group, and those who used the AI-assisted system were assigned to the AI-assisted group. We conducted a 1:1 case–control study that applied propensity score matching to pair the data from patients in a pediatric tertiary hospital between August 1, 2019 and January 31, 2020. Waiting time was defined as the time from registration to preparation for a laboratory test or an imaging examination. The total cost included the registration fee, test fee, examination fee, and drug fee. The Wilcoxon rank-sum test was used to compare the differences in time and cost between the AI-assisted group and the conventional group. The statistical significance level was set at 0.05 for two sides.Results: A total of 12,342 visits were recruited for this study, consisting of 6,171 visits in the conventional group and 6,171 visits in the AI-assisted group. The median waiting time was 0.38 (inter-quartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group (p < 0.05).Conclusions: Using AI can significantly reduce the waiting time of patients for outpatient procedures, and thus, enhance the outpatient process of hospitals.

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