Mobile health (mHealth) technology has assumed a pervasive role in healthcare and society. By capturing real-time features related to spine health, mHealth assessments have the potential to transform multiple aspects of spine care. Yet mHealth applications may not be familiar to many spine surgeons and other spine clinicians. Consequently, the objective of this narrative review is to provide an overview of the technology, analytical considerations, and applications of mHealth tools for evaluating spine surgery patients. Reflecting their near-ubiquitous role in society, smartphones are the most commonly available form of mHealth technology and can provide measures related to activity, sleep, and even social interaction. By comparison, wearable devices can provide more detailed mobility and physiological measures, although capabilities vary substantially by device. To date, mHealth evaluations in spine surgery patients have focused on the use of activity measures, particularly step counts, in an attempt to objectively quantify spine health. However, the correlation between step counts and patient-reported disease severity is inconsistent, and further work is needed to define the mobility metrics most relevant to spine surgery patients. mHealth assessments may also support a variety of other applications that have been studied less frequently, including those that prevent postoperative complications, predict surgical outcomes, and serve as motivational aids to patients. These areas represent key opportunities for future investigations. To maximize the potential of mHealth evaluations, several barriers must be overcome, including technical challenges, privacy and regulatory concerns, and questions related to reimbursement. Despite those obstacles, mHealth technology has the potential to transform many aspects of spine surgery research and practice, and its applications will only continue to grow in the years ahead.
Jacob K. Greenberg, Saad Javeed, Justin K. Zhang, Braeden Benedict, Madelyn R. Frumkin, Ziqi Xu, Jingwen Zhang, Thomas L. Rodebaugh, Chenyang Lu, Jay F. Piccirillo, Michael Steinmetz, Zoher Ghogawala, Mohamad Bydon, and Wilson Z. Ray
Marlise P. dos Santos, Jingwen Zhang, Diana Ghinda, Rafael Glikstein, Ronit Agid, Georges Rodesch, Donatella Tampieri, and Karel G. terBrugge
Intraspinal tumors comprise a large spectrum of neoplasms, including hemangioblastomas, paragangliomas, and meningiomas. These tumors have several common characteristic imaging features, such as highly vascular mass appearance in angiography, hypointense rim and serpentine flow voids in MRI, and intense enhancement after intravenous contrast administration. Due to their rich vascularity, these tumors represent a special challenge for surgical treatment. More recently, the surgical treatment of intraspinal vascular tumors has benefited from the combination of endovascular techniques used to better delineate these lesions and to promote preoperative reduction of volume and tissue blood flow. Endovascular embolization has been proven to be a safe procedure that facilitates the resection of these tumors; hence, it has been proposed as part of the standard of care in their management.
Jingwen Hu, Xin Jin, Jong B. Lee, Liying Zhang, Vipin Chaudhary, Murali Guthikonda, King H. Yang, and Albert I. King
The aims of this study were to develop a three-dimensional patient-specific finite element (FE) brain model with detailed anatomical structures and appropriate material properties to predict intraoperative brain shift during neurosurgery and to update preoperative magnetic resonance (MR) images using FE modeling for presurgical planning.
A template-based algorithm was developed to build a 3D patient-specific FE brain model. The template model is a 50th percentile male FE brain model with gray and white matter, ventricles, pia mater, dura mater, falx, tentorium, brainstem, and cerebellum. Gravity-induced brain shift after opening of the dura was simulated based on one clinical case of computer-assisted neurosurgery for model validation. Preoperative MR images were updated using an FE model and displayed as intraoperative MR images easily recognizable by surgeons. To demonstrate the potential of FE modeling in presurgical planning, intraoperative brain shift was predicted for two additional head orientations.
Two patient-specific FE models were constructed. The mesh quality of the resulting models was as high as that of the template model. One of the two FE models was selected to validate model-predicted brain shift against data acquired on intraoperative MR imaging. The brain shift predicted using the model was greater than that observed intraoperatively but was considered surgically acceptable.
A set of algorithms for developing 3D patient-specific FE brain models is presented. Gravity-induced brain shift can be predicted using this model and displayed on high-resolution MR images. This strategy can be used not only for updating intraoperative MR imaging, but also for presurgical planning.