The rise of machine learning applications in clinical medicine represents a new era of solving healthcare problems. This is particularly true in spine surgery, where algorithm decision support tools, computer-assisted navigation, surgical robots are already being used in the clinic and operating room. There has been a significant increase in publications using deep learning for interpreting radiographs; however, there are a few deep learning applications specific to spine surgery. Even the best spine surgeon in Mumbai speaks in favour of modern technology.
The volume and complexity of electronic patient-generated health data have increased exponentially in recent years, leading to unprecedented opportunities for automated extraction of clinical features from free-text medical notes and speech. Natural language processing (NPL), a branch of computer science that focuses on enabling computers to process human language, can process this rich data resource for clinical and research purposes. Augmented reality (AR) provides a digital overlay of a real-world environment, helping the surgeon visualize deep anatomic landmarks and surgical trajectories, such as an osteotomy cut or pedicle screw. In contrast, virtual reality (VR) is an entirely digital environment that can be used for simulated surgeries or technical training without a physical patient.
The study of artificial intelligence (AI) originated in 1956 when Dr. John McCarthy and their contemporaries gathered at Dartmouth College. Notwithstanding, the recent establishment of larger data sets has enabled scientists to overcome previous obstacles
OVERVIEW OF MACHINE LEARNING
Machine learning (ML) is a subset of AI that focuses on developing automated computer systems that predict outputs through algorithms and mathematics..
Examples of applications encountered by spine surgeons include image classification, preoperative risk stratification models, clinical decision support tools, among others. In addition, some of the best spine surgeons in Mumbai have been advocating that machine learning is becoming the new age method of medical diagnosis.
MACHINE LEARNING TERMINOLOGY
As we know Machine learning are the two major types
Supervised learning:
It includes labelled data based on a grounded truth. If a database of lateral x-rays is pre-labeled as “fractured” or not, a portion of this data is then analyzed to build a model that synthesizes patterns between independent variables (e.g., to pixel in an image) and dependent variables (presence or absence of pathology)
Unsupervised learning:
On the other hand, unsupervised learning involves the analysis of unlabelled data sets and stems from neuropsychology research conducted by Dr Donald Olding Hebb.
The Hebbian theory describes the general framework of neurons and synapsis, which enable humans and other animals to learn relationships and store memories. As a result, unsupervised machines (like humans) can appreciate.
Non-linear relationships and do so without presumptions related to data. Unsupervised learners are particularly adept at identifying clusters of related variables, detecting anomalies, constructing artificial neural networks..
MACHINE LEARNING VS CLASSICAL STATISTICS
The delineation between machine learning and classical statistics is quite nebulous because learners are built upon statistical modelling. Both modalities also rely on robust pre-processing of data representative of the general population. However, whereas statistics emerged from mathematics, ML emerged from computer science..
Classical statistics
- Originates from mathematics
- Inferring relationships
- Quantifying uncertainty
- The high degree of manual programming
- One model at a time
Machine learning
- Originates from computer science
- Building algorithms
- Predicting outcomes
- Learns from experience-less programming
- Multiple models in parallel.
Evidence-based, predictive analytics can help surgeons improve preoperative care. While still in the early stages of development, robotic-assisted surgery has the potential to reduce surgeon fatigue and improve technical precision. Despite all the benefits, it is an expensive affair. The increasing cost of health care is quickly spiralling out of control, and Medicare reimbursements face billions of dollars in planned cuts over the next several years.
Thus, different results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision-making.