Chronic low back pain (CLBP) is a worldwide epidemic, with a prevalence rate of 75–84% in developed countries. With the prevalence increasing, health-care professionals must question current best practice guidelines. In 2014, spinal neurosurgeon and back pain rehabilitation specialist Dr. David Johnson developed a unique back pain rehabilitation program referred to as NearoHAB®. The program’s uniqueness is founded on the principle that effective rehabilitation must eliminate the root cause of pain symptoms. The NeuroHAB® 8-week Movement therapy program aims to reverse movement dysfunction by restoring central nervous system-derived motor patterns based on proficient spinopelvic biomechanics for bending activities of daily living. To date, no other rehabilitation methodology adopts a movement dysfunction cause-based clinical model for back pain symptoms or includes a framework for what healthy lumbar pelvic movement should resemble. Over the course of the 8-week program, each participant is gradually upskilled, developing new default movement proficiency and improved biomechanics, in efforts to downregulate pain, improve disability, and increase functional movement capacity, creating a positive feedback loop for further progress. The leading question of this study is “How does functional movement-based therapy impact chronic low back pain?” Ten sets of participant details were selected at random and retrieved from the NeuroHAB® 8-week program database of 2020. All participants presented with CLBP, and two oswestry disability index (ODI) scores were documented – the first at the beginning of the 8-week program, and the second after the NeuroHAB® intervention. ODI scores were collated and the pre- and post-program results were measured and compared quantitatively through a paired t-test to determine the statistical significance of improvement. Results showed a two-tailed P=0.05 indicating that there was a significant difference between the pre- and post-data (0.0024). The pre- and post-group intervention ODI means were 25.80 and 13.30, respectively, resulting in a difference of 12.50 (95% CI: 5.73–19.27); determining the mean data between the pre- and post-intervention decreased by 48.4496%. The results from this study support the alternative hypothesis, concluding an 8-week intervention of functional movement therapy represented by NeuroHAB® results in a significant reduction of LBP ODI scores.
Introduction: Artificial intelligence (AI) has emerged as a groundbreaking technology with the potential to transform various sectors, and the field of medicine is no exception. With its ability to process vast amounts of data and perform complex tasks, AI has begun to revolutionize healthcare, offering promising avenues for diagnosis, treatment, and patient care. In this editorial article, we will explore the significant impact of AI in medicine, highlighting its potential benefits and the challenges that lie ahead. AI-Driven Diagnosis One of the most remarkable applications of AI in medicine is its capacity to assist in accurate and efficient diagnosis. By leveraging machine learning algorithms, AI systems can analyze medical imaging, such as X-rays, MRIs, and CT scans, with a level of precision that rivals human experts. Studies have demonstrated the effectiveness of AI in detecting various conditions, including lung cancer, cardiovascular diseases, and neurological disorders, leading to earlier and more accurate diagnoses. For instance, a study published in Nature Medicine by McKinney et al. revealed that an AI model trained on a large dataset of mammograms outperformed radiologists in breast cancer detection. The AI system achieved a lower false-negative rate and reduced the number of false positives, thereby potentially reducing unnecessary biopsies [1]. Similarly, a study by Esteva et al., showed that a deep learning algorithm outperformed dermatologists in diagnosing skin cancer based on images [2]. Such advancements in AI-driven diagnosis hold immense promise for improving patient outcomes and reducing healthcare costs. Personalized Treatment and Precision Medicine AI has also opened doors to personalized treatment strategies, enabling healthcare professionals to tailor therapies to individual patients. By analyzing vast amounts of patient data, including genetic information, medical history, and treatment outcomes, AI algorithms can identify patterns, predict responses to specific treatments, and recommend personalized interventions. This approach, known as precision medicine, has the potential to revolutionize disease management. An example of AI's impact on precision medicine is showcased in the work of Poplin et al. The study demonstrated how a deep learning algorithm could predict the onset of cardiovascular events by analyzing electronic health records. The algorithm outperformed traditional risk models by incorporating a broader range of patient data, allowing for more accurate and timely interventions to prevent adverse events [3]. Similarly, Obermeyer et al., demonstrated that an AI model outperformed traditional methods in predicting acute kidney injury in hospitalized patients [4] while a study by Che et al., demonstrated the effectiveness of an AI model in predicting sepsis, allowing for early intervention and improved patient outcomes [5]. Enhanced Clinical Decision-Making and Workflow AI has the capacity to enhance clinical decision-making by assisting healthcare providers in analyzing complex data and generating evidence-based recommendations. AI systems can process and interpret vast amounts of medical literature, patient records, and clinical guidelines, providing healthcare professionals with timely insights and decision support. This augmentation of human expertise can lead to more accurate diagnoses, improved treatment plans, and enhanced patient care. A notable example is the work of Rajkomar et al., published in The New England Journal of Medicine. The authors developed an AI algorithm capable of predicting patient deterioration within the next few hours, based on electronic health record data. By alerting healthcare providers in advance, this AI system helped to prevent adverse events and facilitated proactive interventions [6]. Drug Discovery and Clinical Research The drug discovery and development process is notoriously expensive and time-consuming. AI has the potential to accelerate this process by analyzing vast amounts of biomedical literature, genomic data, and clinical trial outcomes. Machine learning models can identify potential drug targets, predict drug toxicity, and optimize drug formulations. In fact, a study by Aliper et al., demonstrated that an AI system outperformed human researchers in designing new drugs to target age-related diseases [7]. Virtual Assistants and Telemedicine AI-powered virtual assistants and chatbots are transforming the way patients interact with healthcare providers. These virtual assistants can provide instant medical advice, answer queries, and triage patients based on their symptoms. Furthermore, telemedicine platforms integrated with AI algorithms can enhance remote patient monitoring, enabling healthcare professionals to monitor patients' vital signs and provide timely interventions [8,9]. Challenges and Ethical Considerations While the potential benefits of AI in medicine are substantial, it is important to address the challenges and ethical considerations associated with its implementation. Privacy and data security remain critical concerns when handling vast amounts of patient data. Maintaining patient confidentiality and ensuring secure data sharing frameworks must be prioritized to protect patient privacy. Moreover, the need for transparency and interpretability of AI algorithms is vital to build trust between healthcare professionals and AI systems. Understanding how AI arrives at its recommendations or diagnoses is crucial for healthcare providers to make informed decisions and ensure accountability. Conclusion: Artificial intelligence holds tremendous potential to revolutionize healthcare and improve patient outcomes. From enhancing diagnostic accuracy to enabling personalized treatment strategies and augmenting clinical decision-making, AI is transforming the field of medicine. However, to fully realize the benefits, it is essential to address the challenges surrounding privacy, data security, and algorithm transparency. By leveraging the power of AI responsibly, healthcare providers can usher in a new era of precision medicine, advancing the quality and effectiveness of patient care.
Spinal manipulation (SM) has been documented to have various physiological effects, of which the research literature has started to reflect over the past decade. This case study was designed with intent to further investigate these findings. A 31 year old woman with experience of lifting weights and working a very physical job presented with ipsilateral right-sided lower neck and shoulder pain (C7-T4, right trapezius, and right scapula area) and bilateral low back pain (L1-L5 and S.I joint area). Following the examination, a differential diagnosis list was decided on with the input of multiple doctors and therapists. The primary treatment was SM over a time span of 6 months. The patient displayed significant results. Objective testing through a follow-up range of motion (ROM) examination showed an increase in ROM and a spinal examination presented a reduction in local muscle tightness. In addition, subjectively, the patient reported a significant reduction in pain, an increase in movement confidence, and ability. The results of this case study suggest that SM in conjunction with patient education has a significant positive effect on the patient’s reduction of pain, local muscle tightness and increase in ROM, and patient movement ability and confidence. Further studies are required to isolate the specific effects of SM in a high-powered study and clinical setting.
Objectives: Platelet-rich fibrin (PRF) is an autogenous biomaterial, considered as the second-generation platelet concentrates rich in blood cells and growth factors entrapped in the fibrin matrix, which makes it as an ideal material with wound healing abilities. Studies have reported high harvest of cells using anticoagulants but the present study employs two different protocols to efficiently separate the platelet-rich layer at low centrifugal forces without the use of anticoagulants. Material and Methods: Blood samples were collected with the consent of 20 volunteer donors. Ten blood samples were tested in each of the protocols studied, that is, protocol 1–200 g for 8 min (higher rpm and time) and protocol 2–60 g for 5 min (lower rpm and time). From the 12 ml of blood samples collected, 2 ml of blood was utilized for whole blood study; the remaining 10 ml was transferred into falcon tubes subjected to different rpm. Results: The present study employs a novel method to investigate segregation of cell types following low-grade centrifugation. One milliliter sequential pipetting technique was used to analyze number of leukocytes and platelets following centrifugation at two different g-forces. The protocols followed in the present study had 2–3-fold increase leukocytes concentration and 10–12-fold increases in platelet concentrations in the layers than the whole blood sample without the use of anticoagulants. Conclusion: The study concludes that protocol 1 was more efficient in harvesting platelets with less concentration of leukocytes, which is considered more suitable for various medical applications.
