Journal Article10.1007/S00586-021-06799-Z
Artificial intelligence clustering of adult spinal deformity sagittal plane morphology predicts surgical characteristics, alignment, and outcomes.
Wesley M. Durand,Renaud Lafage,D. Kojo Hamilton,Peter G. Passias,Han Jo Kim,Themistocles S. Protopsaltis,Virginie Lafage,Justin S. Smith,Christopher I. Shaffrey,Munish C. Gupta,Michael P. Kelly,Eric O. Klineberg,Frank J. Schwab,Jeffrey L. Gum,Gregory M. Mundis,Robert K. Eastlack,Khaled M. Kebaish,Alex Soroceanu,Richard A. Hostin,Doug Burton,Shay Bess,Christopher P. Ames,Robert A. Hart,Alan H. Daniels +23 more
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TL;DR: In this article, a 2'×'3' self-organizing map was developed to cluster preoperative lateral radiographs of ASD patients into groups with highly distinct overall spinal morphology and association with sagittal alignment parameters, baseline HRQOL, and surgical characteristics.
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Abstract: AI algorithms have shown promise in medical image analysis. Previous studies of ASD clusters have analyzed alignment metrics—this study sought to complement these efforts by analyzing images of sagittal anatomical spinopelvic landmarks. We hypothesized that an AI algorithm would cluster preoperative lateral radiographs into groups with distinct morphology. This was a retrospective review of a multicenter, prospectively collected database of adult spinal deformity. A total of 915 patients with adult spinal deformity and preoperative lateral radiographs were included. A 2 × 3, self-organizing map—a form of artificial neural network frequently employed in unsupervised classification tasks—was developed. The mean spine shape was plotted for each of the six clusters. Alignment, surgical characteristics, and outcomes were compared. Qualitatively, clusters C and D exhibited only mild sagittal plane deformity. Clusters B, E, and F, however, exhibited marked positive sagittal balance and loss of lumbar lordosis. Cluster A had mixed characteristics, likely representing compensated deformity. Patients in clusters B, E, and F disproportionately underwent 3-CO. PJK and PJF were particularly prevalent among clusters A and E. Among clusters B and F, patients who experienced PJK had significantly greater positive sagittal balance than those who did not. This study clustered preoperative lateral radiographs of ASD patients into groups with highly distinct overall spinal morphology and association with sagittal alignment parameters, baseline HRQOL, and surgical characteristics. The relationship between SVA and PJK differed by cluster. This study represents significant progress toward incorporation of computer vision into clinically relevant classification systems in adult spinal deformity. Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding.
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TL;DR: The use of machine learning in the field of orthopaedic research is rapidly expanding as discussed by the authors , which is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data.
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The application of artificial intelligence in spine surgery
S. Zhou,Feifei Zhou,Yu Sun,Xin Chen,Yinze Diao,Yanbin Zhao,Haoge Huang,Xiao Fan,Gang Zhang,Xinhang Li +9 more
TL;DR: It is found that artificial intelligence was widely used in spine surgery and the application scenarios included etiology, diagnosis, treatment, postoperative prognosis and decision support systems of spinal diseases.
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An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery
Rafael De la Garza Ramos,Mousa K Hamad,Jessica Ryvlin,Oscar Krol,Peter G. Passias,Mitchell S. Fourman,John H. Shin,Vijay Yanamadala,Yaroslav Gelfand,Saikiran G. Murthy,Reza Yassari +10 more
TL;DR: Prediction of blood transfusion after adult spinal deformity (ASD) surgery can identify at-risk patients and potentially reduce its utilization and the complications associated with it and the use of artificial neural networks (ANNs) offers the potential for high predictive capability.
Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery: Soft Tissue Imaging May be Necessary for Accurate Models.
Graham Johnson,Hani Chanbour,Mir Amaan Ali,Jeffrey W Chen,Tyler Metcalf,Derek J. Doss,Iyan Younus,Soren Jonzzon,Steven Roth,Amir M. Abtahi,Byron F. Stephens,Scott L. Zuckerman +11 more
TL;DR: The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared with raw scoliosis radiographs and traditional clinical/radiographic measurements, and may indicate that PJK is best predicted by soft tissue degeneration and muscle atrophy.
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An overview of Big Data in Healthcare: multiple angle analyses
Xunjie Gou,Zeshui Xu +1 more
- 31 Aug 2021
TL;DR: An overview of the contents of big data healthcare is provided, including the electronic health records, the medical image data, the healthcare system bigData, the health Internet of Things and healthcare informatics, the remote medical monitoring big Data, the biomedical big data, and other sources ofbig data.
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