Dataset Information

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm.

ABSTRACT: Background and purpose - We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs. Patients and methods - 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4-part, 247) classified by 3 specialists were evaluated. We trained a deep convolutional neural network (CNN) after augmentation of a training dataset. The ability of the CNN, as measured by top-1 accuracy, area under receiver operating characteristics curve (AUC), sensitivity/specificity, and Youden index, in comparison with humans (28 general physicians, 11 general orthopedists, and 19 orthopedists specialized in the shoulder) to detect and classify proximal humerus fractures was evaluated. Results - The CNN showed a high performance of 96% top-1 accuracy, 1.00 AUC, 0.99/0.97 sensitivity/specificity, and 0.97 Youden index for distinguishing normal shoulders from proximal humerus fractures. In addition, the CNN showed promising results with 65-86% top-1 accuracy, 0.90-0.98 AUC, 0.88/0.83-0.97/0.94 sensitivity/specificity, and 0.71-0.90 Youden index for classifying fracture type. When compared with the human groups, the CNN showed superior performance to that of general physicians and orthopedists, similar performance to orthopedists specialized in the shoulder, and the superior performance of the CNN was more marked in complex 3- and 4-part fractures. Interpretation - The use of artificial intelligence can accurately detect and classify proximal humerus fractures on plain shoulder AP radiographs. Further studies are necessary to determine the feasibility of applying artificial intelligence in the clinic and whether its use could improve care and outcomes compared with current orthopedic assessments.

SUBMITTER: Chung SW

PROVIDER: S-EPMC6066766 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Automated detection and classification of the proximal humerus fracture by using deep learning algorithm.

Chung Seok Won SW Han Seung Seog SS Lee Ji Whan JW Oh Kyung-Soo KS Kim Na Ra NR Yoon Jong Pil JP Kim Joon Yub JY Moon Sung Hoon SH Kwon Jieun J Lee Hyo-Jin HJ Noh Young-Min YM Kim Youngjun Y

Acta orthopaedica 20180326 4

Background and purpose - We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs. Patients and methods - 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4-part, 247) classified by 3 specialists were evaluated. We trained a deep convolutional neural network ...[more]

PMID: 29577791

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Project description:Purpose: To assess the accuracy and efficacy of a semi-automated deep learning algorithm (DLA) assisted approach to detect vision-threatening diabetic retinopathy (DR). Methods: We developed a two-step semi-automated DLA-assisted approach to grade fundus photographs for vision-threatening referable DR. Study images were obtained from the Lingtou Cohort Study, and captured at participant enrollment in 2009-2010 ("baseline images") and annual follow-up between 2011 and 2017. To begin, a validated DLA automatically graded baseline images for referable DR and classified them as positive, negative, or ungradable. Following, each positive image, all other available images from patients who had a positive image, and a 5% random sample of all negative images were selected and regraded by trained human graders. A reference standard diagnosis was assigned once all graders achieved consistent grading outcomes or with a senior ophthalmologist's final diagnosis. The semi-automated DLA assisted approach combined initial DLA screening and subsequent human grading for images identified as high-risk. This approach was further validated within the follow-up image datasets and its time and economic costs evaluated against fully human grading. Results: For evaluation of baseline images, a total of 33,115 images were included and automatically graded by the DLA. 2,604 images (480 positive results, 624 available other images from participants with a positive result, and 1500 random negative samples) were selected and regraded by graders. The DLA achieved an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.953, 0.970, 0.879, and 88.6%, respectively. In further validation within the follow-up image datasets, a total of 88,363 images were graded using this semi-automated approach and human grading was performed on 8975 selected images. The DLA achieved an AUC, sensitivity, and specificity of 0.914, 0.852, 0.853, respectively. Compared against fully human grading, the semi-automated DLA-assisted approach achieved an estimated 75.6% time and 90.1% economic cost saving. Conclusions: The DLA described in this study was able to achieve high accuracy, sensitivity, and specificity in grading fundus images for referable DR. Validated against long-term follow-up datasets, a semi-automated DLA-assisted approach was able to accurately identify suspect cases, and minimize misdiagnosis whilst balancing safety, time, and economic cost.

Project description:INTRODUCTION:In locked plate fixation of proximal humerus fractures, the calcar is an important anchor point for screws providing much-needed medial column support. Most locking plate implants utilize a fixed-trajectory locking screw to achieve this goal. Consequently, adjustments of plate location to account for patient-specific anatomy may result in a screw position outside of the calcar. To date, little is known about the consequences of "missing" the calcar during plate positioning. This study sought to characterize the biomechanics associated with proximal and distal placement of locking plates in a two-part fracture model. MATERIALS AND METHODS:This experiment was performed twice, first with elderly cadaveric specimens and again with osteoporotic sawbones. Two-part fractures were simulated and specimens were divided to represent proximal, neutral, and distal plate placements. Non-destructive torsional and axial compression tests were performed prior to an axial fatigue test and a ramp to failure. Torsional stiffness, axial stiffness, humeral head displacement and stiffness during fatigue testing, and ultimate load were compared between groups. RESULTS:Cadavers: Proximal implant placement led to trends of decreased mechanical properties, but there were no significant differences found between groups. Sawbones: Distal placement increased torsional stiffness in both directions (p?=?0.003, p?=?0.034) and axial stiffness (p?=?0.018) when compared to proximal placement. Distal placement also increased torsional stiffness in external rotation (p?=?0.020), increased axial stiffness (p?=?0.024), decreased humeral head displacement during fatigue testing, and increased stiffness during fatigue testing when compared to neutral placement. DISCUSSION:The distal and neutral groups had similar mechanical properties in many cadaveric comparisons while the proximal group trended towards decreased construct stiffness. RESULTS:from the Sawbones model were more definitive and provided further evidence that proximal calcar screw placements are undesirable and distal implant placement may provide improved construct stability. CONCLUSION:Successful proximal humerus fracture reconstruction is inherent upon anatomic fracture reduction coupled with medial column support. Results from this experiment suggest that missing the calcar proximally is deleterious to fixation strength, while it is safe, and perhaps even desirable, to aim slightly distal to the intended target.

Dataset Information

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm.

Publications

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm.

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