Spinal Cord Morphology in Degenerative Cervical Myelopathy Patients; Assessing Key Morphological Characteristics Using Machine Vision Tools.
Spinal Cord Morphology in Degenerative Cervical Myelopathy Patients; Assessing Key Morphological Characteristics Using Machine Vision Tools.
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DOI：
10.3390/jcm10040892
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发表时间：
2021-02-23
影响因子：
3.9
通讯作者：
Cadotte DW
中科院分区：
文献类型：
--
作者：
Ost K;Jacobs WB;Evaniew N;Cohen-Adad J;Anderson D;Cadotte DW
尽管退行性颈脊髓病（DCM）是最常见的脊髓损伤形式，但评估患者是否存在和严重程度的有效方法才刚刚开始出现。对患者图像的评估虽然快速，但往往不可靠; DCM的病理学复杂，临床医生往往难以预测患者的预后。自动化工具，如脊髓造影（SCT），显示出希望，但仍处于早期发展阶段。为了评估SCT自动化过程的当前状态，我们将其应用于328名DCM患者的MR成像记录，使用改良的日本骨科协会量表作为DCM严重程度的衡量标准。我们发现，从这些自动化方法中提取的指标不足以可靠地预测疾病的严重程度。然而，这种自动化进程显示出潜力，突出了未来分析随着时间的推移可以克服的趋势和障碍。这与其他具有类似过程的研究结果相结合，表明可以添加额外的非成像指标以实现诊断相关的预测。尽管诸如此类的建模技术仍处于起步阶段，但DCM严重程度的未来模型可以大大改善自动化临床诊断，与患者的沟通以及患者的预后。
Despite Degenerative Cervical Myelopathy (DCM) being the most common form of spinal cord injury, effective methods to evaluate patients for its presence and severity are only starting to appear. Evaluation of patient images, while fast, is often unreliable; the pathology of DCM is complex, and clinicians often have difficulty predicting patient prognosis. Automated tools, such as the Spinal Cord Toolbox (SCT), show promise, but remain in the early stages of development. To evaluate the current state of an SCT automated process, we applied it to MR imaging records from 328 DCM patients, using the modified Japanese Orthopedic Associate scale as a measure of DCM severity. We found that the metrics extracted from these automated methods are insufficient to reliably predict disease severity. Such automated processes showed potential, however, by highlighting trends and barriers which future analyses could, with time, overcome. This, paired with findings from other studies with similar processes, suggests that additional non-imaging metrics could be added to achieve diagnostically relevant predictions. Although modeling techniques such as these are still in their infancy, future models of DCM severity could greatly improve automated clinical diagnosis, communications with patients, and patient outcomes.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
2.8
作者：
Bednarik, Josef;Kadanka, Zdenek;Kratochvilova, Dagmar
通讯作者：
Kratochvilova, Dagmar
影响因子：
3
作者：
Kopjar, Branko;Bohm, Parker E.;Arnold, Paul M.
通讯作者：
Arnold, Paul M.
影响因子：
2.8
作者：
Evaniew, Nathan;Cadotte, David W.;Jacobs, W. Bradley
通讯作者：
Jacobs, W. Bradley
影响因子：
2.8
作者：
Tetreault, Lindsay;Kopjar, Branko;Fehlings, Michael G.
通讯作者：
Fehlings, Michael G.