This study is a comparative, literature review. The aim of this study is to provide a comparative analysis of open vs. minimally invasive TLIF using a literature review and a meta-analysis. Lumbar interbody fusion is a well-established surgical procedure for treating several spinal disorders. Transforaminal lumbar interbody fusion (TLIF) was initially introduced in the early 1980s. To reduce approach-related morbidity associated with traditional open TLIF (OTLIF), minimally invasive TLIF (MITLIF) was developed. We aimed to provide a comparative analysis of open vs. minimally invasive TLIF using a literature review. We searched the online database PubMed (2005–2017), which yielded an initial 194 studies. We first searched the articles’ abstracts. Based on our inclusion criteria, we excluded 162 studies and included 32 studies: 18 prospective, 13 retrospective, and a single randomized controlled trial. Operative time, blood loss, length of hospital stay, radiation exposure time, complication rate, and pain scores (visual analogue scale, Oswestry Disability Index) for both techniques were recorded and presented as means. We then performed a meta-analysis. The meta-analysis for all outcomes showed reduced blood loss (P < 0.00001) and length of hospital stay (P < 0.00001) for MITLIF compared with OTLIF, but with increased radiation exposure time with MITLIF (P < 0.00001). There was no significant difference in operative time between techniques (P = 0.78). The complication rate was lower with MITLIF (11.3%) vs. OTLIF (14.2%), but not statistically significantly different (P = 0.05). No significant differences were found in visual analogue scores (back and leg) and Oswestry Disability Index scores between techniques, at the final follow-up. MITLIF and OTLIF provide equivalent long-term clinical outcomes. MITLIF had less tissue injury, blood loss, and length of hospital stay. MITLIF is also a safe alternative in obese patients and, in experienced hands, can also be used safely in select cases of spondylodiscitis even with epidural abscess. MITLIF is also a cost-saving procedure associated with reduced hospital and social costs. Long-term studies are required to better evaluate controversial items such as operative time.

Purpose: Apart from patients with severe neurological deficits, it is not clear whether surgical or conservative treatment of lumbar disc herniations is superior for the individual patient. We investigated whether deep learning techniques can predict the outcome of patients with lumbar disc herniation after 6 months of treatment. Methods: The data of 60 patients were used to train and test a deep learning algorithm with the aim to achieve an accurate prediction of the ODI 6 months after surgery or the start of conservative therapy. We developed an algorithm that predicts the ODI of 6 randomly selected test patients in tenfold cross-validation. Results: A 100% accurate prediction of an ODI range could be achieved by dividing the ODI scale into 12% sections. A maximum absolute difference of only 3.4% between individually predicted and actual ODI after 6 months of a given therapy was achieved with our most powerful model. The application of artificial intelligence as shown in this work also allowed to compare the actual patient values after 6 months with the prediction for the alternative therapy, showing deviations up to 18.8%. Conclusion: Deep learning in the supervised form applied here can identify patients at an early stage who would benefit from conservative therapy, and on the contrary avoid painful and unnecessary delays for patients who would profit from surgical therapy. In addition, this approach can be used in many other areas of medicine as an effective tool for decision-making when choosing between opposing treatment options, despite small patient groups.

The treatment options for neuropathic pain caused by lumbar disc herniation have been debated controversially in the literature. Whether surgical or conservative therapy makes more sense in individual cases can hardly be answered. We have investigated whether a machine learning-based prediction of outcome, regarding neuropathic pain development, after lumbar disc herniation treatment is possible. The extensive datasets of 123 consecutive patients were used to predict the development of neuropathic pain, measured by a visual analogue scale (VAS) for leg pain and the Oswestry Disability Index (ODI), at 6 weeks, 6 months and 1 year after treatment of lumbar disc herniation in a machine learning approach. Using a decision tree regressor algorithm, a prediction quality within the limits of the minimum clinically important difference for the VAS and ODI value could be achieved. An analysis of the influencing factors of the algorithm reveals the important role of psychological factors as well as body weight and age with pre-existing conditions for an accurate prediction of neuropathic pain. The machine learning algorithm developed here can enable an assessment of the course of treatment after lumbar disc herniation. The early, comparative individual prediction of a therapy outcome is important to avoid unnecessary surgical therapies as well as insufficient conservative therapies and prevent the chronification of neuropathic pain.

Introduction: The decision to instrument to L5 or ilium, in NMS, is usually based on radiologic factors, including pelvic obliquity (PO) > 15°, apex of curvature < L3, and Cobb angle > 60°. Since scoliosis in these patients is caused by a neurologic disease, we based our decision to stop at L5 on the presence of spasticity or flaccidity. Patients & Methods: The senior author did 109 primary fusions in NMS. Of those with DMD or SMA only 16% were instrumented to the ilium. The main factor for our decision was the correction potential of the truncal shift and PO in the supine traction radiographs and the absence of severe spasticity. Results: The 57 patients with DMD/SMA had a mean preoperative curvature of 68°, PO of 17°, and truncal shift of 20°. 74% should have been instrumented to the pelvis, but only 16% were. Those instrumented shorter as the rule, were corrected from 74° to 26° and had a postoperative PO of 8°. There was no significant difference in postoperative correction and PO compared to those instrumented to L5 on standard protocol. Subsequent extension to the pelvis was needed in 1 CP patient. There were no significant changes after 2 years. Of the 20 patients instrumented to the pelvis 11 had cerebral palsy and a preop curvature of 89°, a PO of 21° and a truncal shift of 25°. Discussion: The decision on instrumentation length should take flexibility and disease into consideration. If the trunk is centred over the pelvis, deterioration will not occur in absence of spasticity.

In recent years, applications using artificial intelligence have been gaining importance in the diagnosis and treatment of spinal diseases. In our review, we describe the basic features of artificial intelligence which are currently applied in the field of spine diagnosis and treatment, and we provide an orientation of the recent technical developments and their applications. Furthermore, we point out the possible limitations and challenges in dealing with such technological advances. Despite the momentary limitations in practical application, artificial intelligence is gaining ground in the field of spine treatment. As an applying physician, it is therefore necessary to engage with it in order to benefit from those advances in the interest of the patient and to prevent these applications being misused by non-medical partners.

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and labor intensive. We developed an open-source library dealing with recurrent tasks in the processing of WSI and helping with the training and evaluation of neuronal networks for classification tasks. Methods: Two histopathology use-cases were selected and only hematoxylin and eosin (H&E) stained slides were used. The first use case was a two-class classification problem. We trained a convolutional neuronal network (CNN) to distinguish between dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG), two neuropathological low-grade epilepsy-associated tumor entities. Within the second use case, we included four clinicopathological disease conditions in a multilabel approach. Here we trained a CNN to predict the hormone expression profile of pituitary adenomas. In the same approach, we also predicted clinically silent corticotroph adenoma. Results: Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. For the second use case, the best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma, and 0.98 for gonadotroph adenoma. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive and fastai-compatible library is helpful to standardize the workflow and minimize the burden of training a CNN. Indeed, our trained CNNs extracted neuropathologically relevant information from the WSI. This approach will supplement the clinicopathological diagnosis of brain tumors, which is currently based on cost-intensive microscopic examination and variable panels of immunohistochemical stainings.