Determining the short-term neurological prognosis for acute cervical spinal cord injury using machine learning

Okimatsu, Sho; Maki, Satoshi; Furuya, Takeo; Fujiyoshi, Takayuki; Kitamura, Mitsuhiro; Inada, Taigo; Aramomi, Masaaki; Yamauchi, Tomonori; Miyamoto, Takuya; Inoue, Takaki; Yunde, Atsushi; Miura, Masataka; Inage, Kazuhide; Orita, Sumihisa; Eguchi, Yawara; Ohtori, Seiji

doi:10.1016/j.jocn.2021.11.037

Cited by 19 publications

(12 citation statements)

References 21 publications

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“…Comparing the results with those found in the study by Okimatsu et al (2022) , the test accuracy here of 73.6% is an improvement over the MRI CNN accuracy of 71.4%. Furthermore, the Ridge Classifier is much easier to interpret and is a more time-efficient model to train.…”

Section: Discussionsupporting

confidence: 70%

“…The inclusion of imagery from MRI as a set of features is a possible route of future research that could further bolster the Ridge Classifier as well. In contrast to the research performed in studies by Inoue et al (2020) , Fan et al (2021) , Buri et al (2022) , Chou et al (2022) , and Okimatsu et al (2022) as a whole, the study conducted here uses a patient base one to two times larger, while including a comprehensive review of feature importance as well. To add on, the results are, overall, comparable or better while considering all AIS classes and using a very lightweight model that can be much more easily deployed.…”

Section: Discussionmentioning

confidence: 99%

“…These three features were suitable because they could capture patient physical improvements throughout the entire body. The AIS score at discharge was ultimately chosen because of its widespread use in the literature ( Roberts et al, 2017 ; Inoue et al, 2020 ; Buri et al, 2022 ; Chou et al, 2022 ; Okimatsu et al, 2022 ). Using this target variable meant that a five-class classification modeling approach was to be designed, where each class is one of A, B, C, D, or E. With this definition, there was a possibility that some patient predictions could include worsening of the AIS score as well.…”

Section: Methodsmentioning

confidence: 99%

“…Researchers have developed many tools on SCI treatment; however, after literature review, it was seen that there is a wide gap in the use of machine learning algorithms to predict SCI recovery in a contemporary precision medicine context, especially with regard to feature importance and using a very large dataset ( Snoek et al, 2004 ; Munce et al, 2014 ). One study attempted to predict discharge location using an ensemble model and used area under the curve as an outcome ( Fan et al, 2021 ), another study made use of convolutional neural nets (CNNs) on MRI charts to achieve an accuracy of 71.4% ( Okimatsu et al, 2022 ), while two other studies greatly limited the complexity to specific AIS scores of A ( Buri et al, 2022 ) and D/E ( Inoue et al, 2020 ). The authors in the study by Chou et al (2022) conducted a study similar to the one presented here, but their sample size consisted of 74 patients.…”

Section: Introductionmentioning

confidence: 99%

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Spinal Cord Injury AIS Predictions Using Machine Learning

Kapoor

2022

eNeuro

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Objective: To use machine learning to predict AIS scores for newly injured SCI patients at hospital discharge time from hospital admission data. Additionally, to analyze the best model for feature importance in order to validate the criticality of AIS score and highlight relevant demographic details.Design: Data used for training machine learning models was from the NSCISC database of United States SCI patient details. 18 real features were used from 417 provided ones, which mapped to 53 machine learning features after processing. 8 models were tuned on the dataset to predict AIS scores and Shapely analysis was performed to extract the most important of the 53 features.Participants: Patients within the NSCISC database who sustained injuries between 1972 and 2016 after data cleaning (n = 20,790).Outcome Measures: Test set multi-class and aggregated Shapely score magnitudes.Results: Ridge Classifier was the best performer with 73.6% test set accuracy. AIS scores and neurologic category at admission time were the best predictors of recovery. Demographically, features were less important but age, sex, marital status, and race stood out. AIS scores on admission are highly predictive of patient outcomes when combined with patient demographic data.Conclusion: Promising results in terms of predicting recovery were seen and Shapely analysis allowed for the machine learning model to be probed as whole, giving insight into overall feature trends.SignificanceThe research is intended to introduce the use of machine learning to enhance predictive capabilities of spinal cord injury recovery, to validate previous motor-sensory classification work, and to extract important deciders of recovery from constructed models.

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Section: Discussionsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spinal Cord Injury AIS Predictions Using Machine Learning

Kapoor

2022

eNeuro

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“…As a result, a total of 6 studies were reviewed. [16,17,[21][22][23][24] All were retrospective designed and the etiology of the subjects was trauma. Two studies focused on walking ability prediction measured by Functional Independence Measure, and other 2 studies predicted functional independence using Spinal Cord Independence Measure, and the others tried to predict AIS grade after injury.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury

Yoo,

Koo,

Yong

et al. 2024

Medicine

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With advances in artificial intelligence, machine learning (ML) has been widely applied to predict functional outcomes in clinical medicine. However, there has been no attempt to predict walking ability after spinal cord injury (SCI) based on ML. In this situation, the main purpose of this study was to predict gait recovery after SCI at discharge from an acute rehabilitation facility using various ML algorithms. In addition, we explored important variables that were related to the prognosis. Finally, we attempted to suggest an ML-based decision support system (DSS) for predicting gait recovery after SCI. Data were collected retrospectively from patients with SCI admitted to an acute rehabilitation facility between June 2008 to December 2021. Linear regression analysis and ML algorithms (random forest [RF], decision tree [DT], and support vector machine) were used to predict the functional ambulation category at the time of discharge (FAC_DC) in patients with traumatic or non-traumatic SCI (n = 353). The independent variables were age, sex, duration of acute care and rehabilitation, comorbidities, neurological information entered into the International Standards for Neurological Classification of SCI worksheet, and somatosensory-evoked potentials at the time of admission to the acute rehabilitation facility. In addition, the importance of variables and DT-based DSS for FAC_DC was analyzed. As a result, RF and DT accurately predicted the FAC_DC measured by the root mean squared error. The root mean squared error of RF and the DT were 1.09 and 1.24 for all participants, 1.20 and 1.06 for those with trauma, and 1.12 and 1.03 for those with non-trauma, respectively. In the analysis of important variables, the initial FAC was found to be the most influential factor in all groups. In addition, we could provide a simple DSS based on strong predictors such as the initial FAC, American Spinal Injury Association Impairment Scale grades, and neurological level of injury. In conclusion, we provide that ML can accurately predict gait recovery after SCI for the first time. By focusing on important variables and DSS, we can guide early prognosis and establish personalized rehabilitation strategies in acute rehabilitation hospitals.

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