Assessment of Usefulness of Anthropometric Data for Predicting the Scaphoid and the Screw Length: A New Technique

Jain, Darshan Kumar A; Shetty, Naresh; Kumar, Lohit; Sundaresh, D.C.

doi:10.1142/s0218810417500460

Cited by 3 publications

(3 citation statements)

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“…It does not require any model training, but does not objectively represent correlations when the correlations between the variables are complex. There is also a risk of multicollinearity between features [91,92]. In this study, the accuracy of the model construction, based on the preferred features under feature selection, was better than that of the model under the full feature condition, which was consistent with the findings of Hsu et al 2011 [93] and validated the effectiveness and generalizability of the feature selection method.…”

Section: Discussionsupporting

confidence: 88%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

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Section: Discussionsupporting

confidence: 88%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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“…Bone union on was routinely performed at 6 months postoperatively to confirm the union status, which was defined as bone bridging over 50% on the nonunion site [13,14]. Radiographic outcomes were evaluated with the following five parameters: scaphoid axial length [15], scapholunate angle (normal range, 30 to 60°; humpback deformity > 60°) [16], radiolunate angle (normal range, 0 to 10°; DISI deformity > 15°) [17,18], carpal height ratio (normal range, 0.5; carpal collapse < 0.45) [19], and the lateral intrascaphoid angle (normal range, < 35°; humpback deformity > 35°) [20].…”

Section: Radiographic Examinationmentioning

confidence: 99%

“…The operated hand was measured as a percentage of the normal side. Considering whether the dominant or non-dominant hand was injured, we employed the 10% rule for data correction [13][14][15]. The active wrist flexion-extension arcs for the operated and non-operated hands were measured with the manual universal goniometer.…”

Section: Clinical Evaluationsmentioning

confidence: 99%

Wrist function recovery course in patients with scaphoid nonunion treated with combined volar bone grafting and a dorsal antegrade headless screw

et al. 2020

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Background Surgical treatment is necessary for scaphoid nonunion. Open surgery with a combined volar and dorsal approach is thought to have poor functional outcomes and a prolonged recovery course. However, the detailed recovery course for this approach is rarely reported. The aim of this study was to investigate the recovery course and radiographic outcome for patients with scaphoid nonunion who underwent a combined volar bone grafting and dorsal antegrade headless screw approach. Material and methods Eighteen patients with scaphoid nonunion who underwent combined volar bone grafting and dorsal antegrade headless screw fixation were enrolled in this retrospective study. Preoperative and serial postoperative wrist functional and radiographic outcomes were collected and analysed. Results All 18 patients achieved bone union at a mean time of 14.3 weeks. Compared to the preoperative status, the grip strength, wrist motion arc, and Mayo Wrist score were improved significantly 6 months after surgery, whilst the Disabilities of the Arm, Shoulder, and Hand (DASH) score did not recover until 12 months after surgery. Significant improvements were found in all scaphoid radiographic parameters. Conclusion The surgical outcomes for scaphoid nonunion treated with a combined volar bone grafting and dorsal antegrade headless screw achieved a high union rate, with great wrist functional and radiographic outcomes. The earliest recovered wrist functional parameters were grip strength, motion arc, Mayo Wrist score and finally the DASH score at postoperative 6 months and 12 months, respectively.

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Preoperative Prediction of the Scaphoid Screw Length for the Fixation of Fractures Using MRI Measurements of Finger Phalanges

Prabhu,

Kamble,

Panchal

et al. 2024

Cureus

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Introduction: Scaphoid fractures are one of the most common carpal bone fractures, with most fractures involving the waist. When surgery is indicated, internal fixation with screws is the standard method for the fixation of these fractures. Accurate length and trajectory of the screw are two crucial parameters essential for optimal internal fixation. Determining the accurate screw length required for fixation can be challenging, especially in an intraoperative setting. Preoperative determination of the screw length can improve surgical precision, thereby contributing to an improved functional outcome. This study investigates a novel approach using magnetic resonance imaging (MRI) measurements of the little, ring, middle, and index finger middle phalanges to preoperatively predict the screw length used for scaphoid fracture fixation.Methods: This retrospective observational study included an analysis of 30 MRIs of the wrist and hand. The MRI protocol for wrist and hand was unified for all the patients to ensure consistency and reproducibility. All MRIs were performed by a single experienced radiologist using a high-field 1.5 Tesla MRI scanner. The axial lengths of the bones were calculated on T1-weighted coronal MRI slices using the Meddiff Rispacs DICOM viewer software. A line was drawn from the condyles of the phalanx to the base of the phalanx to measure the maximum axial length of the middle phalanx. The maximum axial length of the scaphoid was measured from the proximal pole to the distal articular surface along the fracture fixation axis. The measurements were performed by a consultant orthopedic hand surgeon and a qualified orthopedic senior resident to minimize inter-observer errors. A musculoskeletal radiologist further analyzed the measurements. Pearson's correlation coefficient was used to determine the relationship between the scaphoid length and the lengths of the middle phalanges. Paired t-tests were applied with the level of significance set at p < 0.05. Linear regression was performed to develop a predictive equation for scaphoid length based on the middle phalanx measurements.Results: The mean scaphoid length was 1.96 cm. The mean lengths of the middle phalanges of the little, ring, middle, and index fingers were 1.66 cm, 2.05 cm, 2.29 cm, and 1.79 cm, respectively. Using the paired t-test, a significant positive correlation was found between the scaphoid length and middle phalanx length of all fingers (p < 0.001), with the strongest correlation observed with the length of the middle phalanx of the ring finger (r = 0.861). The regression equation for predicting scaphoid length based on the middle phalanx of the middle finger was: y = 0.665 + 0.565x (study power 90%).Conclusion: MRI measurements of the middle phalanx, particularly of the ring finger, offer a reliable method for predicting scaphoid screw length preoperatively. This approach can improve surgical planning, precision, and potentially patient outcomes without increasing additional costs.

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Assessment of Usefulness of Anthropometric Data for Predicting the Scaphoid and the Screw Length: A New Technique

Abstract: A preoperative radiograph of the wrist with hand will help us indirectly assess the axial length of the scaphoid by measuring the axial length of the middle phalanx of ring finger.

Cited by 3 publications

References 10 publications

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Wrist function recovery course in patients with scaphoid nonunion treated with combined volar bone grafting and a dorsal antegrade headless screw

Preoperative Prediction of the Scaphoid Screw Length for the Fixation of Fractures Using MRI Measurements of Finger Phalanges

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