Intelligence-Based Spine Care Model: A New Era of Research and Clinical Decision-Making

Mallow, G. Michael; Siyaji, Zakariah K.; Galbusera, Fabio; Espinoza-Orías, Alejandro; Giers, Morgan B.; Ames, Christopher P.; Karppinen, Jaro; Louie, Philip; Phillips, Frank M.; Pourzal, Robin; Schwab, Joseph H.; Sciubba, Daniel M.; Wang, Jeffrey C.; Wilke, H.-J.; Williams, Frances; Mohiuddin, Shoeb A.; Makhni, Melvin C.; Shepard, Nicholas; An, Howard S.; Samartzis, Dino

doi:10.1177/2192568220973984

Cited by 30 publications

(26 citation statements)

References 116 publications

(125 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There was no thorough data review and no explicit measures to ensure data quality. In line with current ideas on the establishment of artificial intelligence as discussed in the current literature, we see this as the main reason for the not entirely convincing predictive power of this AI model [2][3][4]18,20].…”

Section: Prediction Of Efficiancy Of Conservative Treatment Of Back Painsupporting

confidence: 79%

“…In the meantime, it has been shown several times that, using modern techniques, predictions can also be made for these patients with increasing accuracy [2][3][4][5].…”

Section: Artificial Intelligence In Treatment Of Back Painmentioning

confidence: 99%

“…In recent years, there have been increasingly promising efforts to improve medical care in the field of back pain and spinal therapy through the application of artificial Diagnostics 2021, 11,1934 2 of 10 intelligence [2][3][4][5][6]. Typically, two approaches are followed to establish an AI dealing with medical issues.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

Wirries¹,

Geiger²,

Hammad³

et al. 2021

Diagnostics

View full text Add to dashboard Cite

Patients with back pain are common and present a challenge in everyday medical practice due to the multitude of possible causes and the individual effects of treatments. Predicting causes and therapy efficien cy with the help of artificial intelligence could improve and simplify the treatment. In an exemplary collective of 1000 conservatively treated back pain patients, it was investigated whether the prediction of therapy efficiency and the underlying diagnosis is possible by combining different artificial intelligence approaches. For this purpose, supervised and unsupervised artificial intelligence methods were analyzed and a methodology for combining the predictions was developed. Supervised AI is suitable for predicting therapy efficiency at the borderline of minimal clinical difference. Non-supervised AI can show patterns in the dataset. We can show that the identification of the underlying diagnostic groups only becomes possible through a combination of different AI approaches and the baseline data. The presented methodology for the combined application of artificial intelligence algorithms shows a transferable path to establish correlations in heterogeneous data sets when individual AI approaches only provide weak results.

show abstract

Section: Prediction Of Efficiancy Of Conservative Treatment Of Back Painsupporting

confidence: 79%

“…In the meantime, it has been shown several times that, using modern techniques, predictions can also be made for these patients with increasing accuracy [2][3][4][5].…”

Section: Artificial Intelligence In Treatment Of Back Painmentioning

confidence: 99%

See 1 more Smart Citation

Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

Wirries¹,

Geiger²,

Hammad³

et al. 2021

Diagnostics

View full text Add to dashboard Cite

show abstract

“…At the current time, postoperative instructions for patients with spine implants are qualitative (do as much as you can until the pain starts), creating fears in both the patient and the surgeon [ 36 ]. Questions regarding how much bending, lifting, and exercising is permissible after a cervical implant operation could be studied and correlated with biomechanical data generated by the sensorized robotic replica with individually tailored postoperative care that could be prescribed to reduce complications [ 37 ]. However, one limitation in this study is that there are no ligaments or muscles in the robotic spine replica.…”

Section: Discussionmentioning

confidence: 99%

Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery

Lin

Moaed

Taing

et al. 2021

Sensors

View full text Add to dashboard Cite

Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person’s spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants.

show abstract

“…Image analysis plays an essential role in the identification of different abnormalities. Hence, intelligence is applied to analysis imaging technique that reduced human intervention to make clinical and research decisions in the medical domain [ 19 ]. The deep learning approach recently shows an impressive performance in different fields: object detection, classification, and segmentation.…”

Section: Introductionmentioning

confidence: 99%

Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images

Agarwala

Kumar

Dhara

et al. 2021

Pattern Recognit. Image Anal.

View full text Add to dashboard Cite

In this paper, automated detection of interstitial lung disease patterns in high resolution computed tomography images is achieved by developing a faster region-based convolutional network based detector with GoogLeNet as a backbone. GoogLeNet is simplified by removing few inception models and used as the backbone of the detector network. The proposed framework is developed to detect several interstitial lung disease patterns without doing lung field segmentation. The proposed method is able to detect the five most prevalent interstitial lung disease patterns: fibrosis, emphysema, consolidation, micronodules and ground-glass opacity, as well as normal. Five-fold cross-validation has been used to avoid bias and reduce over-fitting. The proposed framework performance is measured in terms of F-score on the publicly available MedGIFT database. It outperforms state-of-the-art techniques. The detection is performed at slice level and could be used for screening and differential diagnosis of interstitial lung disease patterns using high resolution computed tomography images.

show abstract

Intelligence-Based Spine Care Model: A New Era of Research and Clinical Decision-Making

Cited by 30 publications

References 116 publications

Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery

Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images

Contact Info

Product

Resources

About