Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.

Background: Ex vivo fluorescent confocal microscopy (FCM) is a novel and effective method for a fast-automatized histological tissue examination. In contrast, conventional diagnostic methods are primarily based on the skills of the histopathologist. In this study, we investigated the potential of convolutional neural networks (CNNs) for automatized classification of oral squamous cell carcinoma via ex vivo FCM imaging for the first time. Material and Methods: Tissue samples from 20 patients were collected, scanned with an ex vivo confocal microscope immediately after resection, and investigated histopathologically. A CNN architecture (MobileNet) was trained and tested for accuracy. Results: The model achieved a sensitivity of 0.47 and specificity of 0.96 in the automated classification of cancerous tissue in our study. Conclusion: In this preliminary work, we trained a CNN model on a limited number of ex vivo FCM images and obtained promising results in the automated classification of cancerous tissue. Further studies using large sample sizes are warranted to introduce this technology into clinics.

Background: Brown tumor is a rare skeletal manifestation of secondary hyperparathyroidism. Although diagnosis of the disease is increasingly seen in early stages due to improved screening techniques, some patients still present in a progressed disease stage. The treatment depends on tumor mass and varies from a conservative approach with supportive parathyroidectomy to extensive surgical resection with subsequent reconstruction. Case presentation: We report a case of extensive mandibular brown tumor in a patient with a history of systemic lupus erythematosus, chronic kidney disease, and secondary hyperparathyroidism. Following radical resection of the affected bone, reconstruction could be successfully performed using a free flap. Conclusions: There were no signs of recurrence during five years of close follow-up. Increased awareness and multidisciplinary follow-ups could allow early diagnosis and prevent the need for radical therapeutical approaches.

Real‐time microscopic imaging of freshly excised tissue enables a rapid bedside‐pathology. A possible application of interest is the detection of oral squamous cell carcinomas (OSCCs). The aim of this study was to analyze the sensitivity and specificity of ex vivo fluorescence confocal microscopy (FCM) for OSCCs and to compare confocal images visually and qualitatively with gold standard histopathology. Two hundred eighty ex vivo FCM images were prospectively collected and evaluated immediately after excision. Every confocal image was blindly assessed for the presence or absence of malignancy by two clinicians and one pathologist. The results were compared with conventional histopathology with hematoxylin and eosin staining. OSCCs were detected with a very high sensitivity of 0.991, specificity of 0.9527, positive predictive value of 0.9322 and negative predictive value of 0.9938. The results demonstrate the potential of ex vivo FCM in fresh tissue for rapid real‐time surgical pathology.

Background: Real‐time microscopic imaging of freshly excised oral squamous cell carcinomas (OSCCs) would be potentially supportive in rapid recognition of oral malignancy and an optimal and time‐saving management of patients' surgical treatment.Objectives: The aim of this study was to examine oral squamous cell cancer tissue in regards to the commonly known and well‐described histomorphologic criteria for the diagnosis of OSCC in ex vivo confocal fluorescent microscopy and to analyze its correlation with grade of differentiation and level of invasiveness.Methods: Ex vivo confocal laser scanning microscopy (CLSM) images of 38 OSCCs were evaluated immediately after excision for presence or absence of various cytological and architectural features based on the histopathological background. Next, these features were compared to the grade of differentiation as elaborated via gold standard histologic examination.Results: Of 38 invasive OSCCs, 14 were well differentiated, while three moderately and 19 were poorly differentiated. The presence of the commonly known cytologic and histopathologic criteria for the diagnosis of oral squamous cell carcinoma such as the destruction of the basal cell membrane, cellular and nuclear pleomorphism, anisocytosis, intraepithelial keratinization, nuclear hyperchromasia, atypical mitotic figures as well as the presence of necrosis, and mixed inflammation could be observed in ex vivo fluorescence confocal microscopy (FCM). In ex vivo fluorescence confocal microscopy pictures, cellular pleomorphism and anisocytosis were observed more often in poorly differentiated OSCCs. Intraepithelial keratinization was associated with well differentiated and moderately differentiated OSCCs.Conclusion: The results demonstrate the high potential of ex vivo fluorescence confocal microscopy in fresh tissue for rapid real‐time diagnosis of OSCC.