Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)’s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. Results: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model’s ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. Conclusion We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Biportal endoscopic spinal surgery (BESS) with interbody fusion is a relatively novel minimally invasive technique that was developed to reduce soft tissue trauma and intraoperative blood loss and shorten recovery time while achieving comparable clinical outcomes for lumbar degenerative diseases. Despite the growing interest in BESS, a comprehensive analysis of its effectiveness, complication rates, and long-term outcomes remains lacking. This systematic review evaluated the clinical outcomes, surgical efficacy, and complication rates of BESS with interbody fusion for lumbar degenerative diseases. Recent literature on endoscopic lumbar interbody fusion was included to expand the scope and gain new perspectives, thereby, providing a comparative analysis that highlighted the advantages, limitations, and emerging trends in minimally invasive spine surgery. This review synthesized current evidence to guide future research and clinical applications. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and using a combination of MeSH (Medical Subject Headings) terms and relevant keywords, PubMed/Medline and Scopus databases were systematically searched for studies published between January 2000 and September 2024. The studies were assessed using the ROBINS-I (Risk of Bias in Nonrandomized Studies of Interventions) tool to determine the risk of bias. From the 12 studies that provided clinical evidence, the data extracted were patient demographics; operative time; blood loss; clinical outcomes, such as Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores and fusion rates; and complications. The mean operative time ranged from 98 to 206 minutes, with fusion rates between 70% and 95%. Most studies reported significant improvements in VAS scores for back and leg pain and ODI scores. Complications, including dural tears (2.9%–6.4%) and hematomas (1.4%–4.3%), were infrequent but notable. BESS with interbody fusion demonstrated excellent clinical outcomes, high fusion rates, and few complications. Although these results are promising, more randomized controlled trials and long-term studies are required to confirm the broader applicability, particularly in more complex or multilevel spinal pathologies.

Ossification of the posterior longitudinal ligament (OPLL) is a significant spinal condition that can lead to severe neurological deficits. Recent advancements in machine learning (ML) and deep learning (DL) have led to the development of promising tools for the early detection and diagnosis of OPLL. This systematic review evaluated the diagnostic performance of ML and DL models and clinical implications in OPLL detection. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/Medline and Scopus databases were searched for studies published between January 2000 and September 2024. Eligible studies included those utilizing ML or DL models for OPLL detection using imaging data. All studies were assessed for the risk of bias using appropriate tools. The key performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were analyzed. Eleven studies, comprising a total of 6,031 patients, were included. The ML and DL models demonstrated high diagnostic performance, with accuracy rates ranging from 69.6% to 98.9% and AUC values up to 0.99. Convolutional neural networks and random forest models were the most used approaches. The overall risk of bias was moderate, and concerns were primarily related to participant selection and missing data. In conclusion, ML and DL models show great potential for accurate detection of OPLL, particularly when integrated with imaging techniques. However, to ensure clinical applicability, further research is warranted to validate these findings in more extensive and diverse populations.

Biportal endoscopic spinal surgery (BESS) with interbody fusion is a relatively novel minimally invasive technique that was developed to reduce soft tissue trauma and intraoperative blood loss and shorten recovery time while achieving comparable clinical outcomes for lumbar degenerative diseases. Despite the growing interest in BESS, a comprehensive analysis of its effectiveness, complication rates, and long-term outcomes remains lacking. This systematic review evaluated the clinical outcomes, surgical efficacy, and complication rates of BESS with interbody fusion for lumbar degenerative diseases. Recent literature on endoscopic lumbar interbody fusion was included to expand the scope and gain new perspectives, thereby, providing a comparative analysis that highlighted the advantages, limitations, and emerging trends in minimally invasive spine surgery. This review synthesized current evidence to guide future research and clinical applications. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and using a combination of MeSH (Medical Subject Headings) terms and relevant keywords, PubMed/Medline and Scopus databases were systematically searched for studies published between January 2000 and September 2024. The studies were assessed using the ROBINS-I (Risk of Bias in Nonrandomized Studies of Interventions) tool to determine the risk of bias. From the 12 studies that provided clinical evidence, the data extracted were patient demographics; operative time; blood loss; clinical outcomes, such as Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores and fusion rates; and complications. The mean operative time ranged from 98 to 206 minutes, with fusion rates between 70% and 95%. Most studies reported significant improvements in VAS scores for back and leg pain and ODI scores. Complications, including dural tears (2.9%–6.4%) and hematomas (1.4%–4.3%), were infrequent but notable. BESS with interbody fusion demonstrated excellent clinical outcomes, high fusion rates, and few complications. Although these results are promising, more randomized controlled trials and long-term studies are required to confirm the broader applicability, particularly in more complex or multilevel spinal pathologies.

Ossification of the posterior longitudinal ligament (OPLL) is a significant spinal condition that can lead to severe neurological deficits. Recent advancements in machine learning (ML) and deep learning (DL) have led to the development of promising tools for the early detection and diagnosis of OPLL. This systematic review evaluated the diagnostic performance of ML and DL models and clinical implications in OPLL detection. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/Medline and Scopus databases were searched for studies published between January 2000 and September 2024. Eligible studies included those utilizing ML or DL models for OPLL detection using imaging data. All studies were assessed for the risk of bias using appropriate tools. The key performance metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were analyzed. Eleven studies, comprising a total of 6,031 patients, were included. The ML and DL models demonstrated high diagnostic performance, with accuracy rates ranging from 69.6% to 98.9% and AUC values up to 0.99. Convolutional neural networks and random forest models were the most used approaches. The overall risk of bias was moderate, and concerns were primarily related to participant selection and missing data. In conclusion, ML and DL models show great potential for accurate detection of OPLL, particularly when integrated with imaging techniques. However, to ensure clinical applicability, further research is warranted to validate these findings in more extensive and diverse populations.

Biportal endoscopic spinal surgery (BESS) with interbody fusion is a relatively novel minimally invasive technique that was developed to reduce soft tissue trauma and intraoperative blood loss and shorten recovery time while achieving comparable clinical outcomes for lumbar degenerative diseases. Despite the growing interest in BESS, a comprehensive analysis of its effectiveness, complication rates, and long-term outcomes remains lacking. This systematic review evaluated the clinical outcomes, surgical efficacy, and complication rates of BESS with interbody fusion for lumbar degenerative diseases. Recent literature on endoscopic lumbar interbody fusion was included to expand the scope and gain new perspectives, thereby, providing a comparative analysis that highlighted the advantages, limitations, and emerging trends in minimally invasive spine surgery. This review synthesized current evidence to guide future research and clinical applications. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and using a combination of MeSH (Medical Subject Headings) terms and relevant keywords, PubMed/Medline and Scopus databases were systematically searched for studies published between January 2000 and September 2024. The studies were assessed using the ROBINS-I (Risk of Bias in Nonrandomized Studies of Interventions) tool to determine the risk of bias. From the 12 studies that provided clinical evidence, the data extracted were patient demographics; operative time; blood loss; clinical outcomes, such as Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores and fusion rates; and complications. The mean operative time ranged from 98 to 206 minutes, with fusion rates between 70% and 95%. Most studies reported significant improvements in VAS scores for back and leg pain and ODI scores. Complications, including dural tears (2.9%–6.4%) and hematomas (1.4%–4.3%), were infrequent but notable. BESS with interbody fusion demonstrated excellent clinical outcomes, high fusion rates, and few complications. Although these results are promising, more randomized controlled trials and long-term studies are required to confirm the broader applicability, particularly in more complex or multilevel spinal pathologies.