Lower back pain (LBP) is a leading cause of disability globally, presenting a complex diagnostic and therapeutic challenge. This article reviews the current landscape of AI applications in LBP, focusing on diagnostic, prognostic, and treatment algorithm development.

Traditional approaches for lower back pain management often rely on subjective assessments and can lead to variable outcomes. Artificial intelligence (AI) offers a promising avenue for developing more robust and personalised LBP management algorithms.

Lower Back Pain LBP

Lower back pain LBP represents a significant burden on healthcare systems and individuals worldwide [1]. Affecting a large majority of adults at some point in their lives, LBP stems from a heterogeneous group of underlying causes, ranging from musculoskeletal issues to neurological and psychological factors [2]. This complexity poses a significant challenge for clinicians attempting to accurately diagnose and effectively manage the condition.

Traditional methods often rely on a combination of patient history, physical examination, and imaging, with many aspects dependent on subjective interpretation. This lack of uniformity can lead to delayed or incorrect diagnoses, inappropriate treatment, and subsequently, suboptimal patient outcomes [3]. Artificial intelligence (AI) has emerged as a powerful tool with the potential to transform healthcare practices. Its capacity to analyse large datasets, identify complex patterns, and generate predictions makes it particularly well-suited to tackle the challenges associated with LBP [4].

AI Techniques in LBP Algorithm Development

A range of AI techniques is being explored for their applicability in LBP, each offering unique advantages. These include:

1. Machine Learning (ML): ML algorithms, such as support vector machines (SVM), random forests (RF), and logistic regression, are widely utilised for classification and prediction tasks [5]. In the context of LBP, ML can be trained on large datasets of clinical information, imaging results, and patient-reported outcomes to identify predictive factors for specific LBP subtypes or treatment responses. For instance, ML can help predict the likelihood of developing chronic LBP based on initial symptom presentation [6].
2. Deep Learning (DL): DL, a subset of ML employing artificial neural networks (ANN), excels at pattern recognition in complex data such as medical images [7]. Convolutional Neural Networks (CNNs) are used to analyse MRI and X-ray images, aiding in the detection of vertebral pathologies, disc degeneration, and nerve compressions [8]. Recurrent Neural Networks (RNNs) are suitable for processing time-series data like patient history trajectories or pain level fluctuations, thereby providing insights into the evolving nature of LBP conditions [9].
3. Natural Language Processing (NLP): NLP is used to analyse unstructured text data such as physician notes, patient questionnaires, and research articles [10]. By extracting key information from these sources, NLP can facilitate a comprehensive understanding of patient characteristics, risk factors, and treatment outcomes. It can also expedite literature reviews and knowledge synthesis on LBP [11].
4. Hybrid Approaches: Combining multiple AI techniques often yields more robust models. For instance, a hybrid model might use NLP to extract pertinent information from clinical notes and then employ ML algorithms to predict treatment efficacy.

 Applications of AI in LBP Algorithm Development

AI is impacting LBP management across various stages:

1. Diagnostic Algorithms: AI-powered algorithms are being developed to assist in the differentiation of various LBP etiologies. For example, DL models trained on MRI scans can identify characteristic features associated with specific spinal pathologies. ML classifiers can combine clinical history, physical examination data, and imaging results to predict the likelihood of specific causes of LBP [12]. This can lead to more targeted and efficient diagnostic workflows, reducing unnecessary or inappropriate investigations.
2. Prognostic Algorithms: AI can predict the prognosis of LBP, including the likelihood of chronicity, disability, and treatment response. By analysing risk factors and symptom patterns, these algorithms can identify individuals at higher risk and enable early intervention [13]. ML has been shown to be promising in predicting which LBP patients are more likely to develop long-term limitations [14].
3. Treatment Algorithms: AI can be utilised to develop personalised treatment algorithms tailored to individual patient profiles. Based on predictive modelling, AI algorithms can recommend appropriate interventions, such as specific exercises, medications, or psychological therapies, based on the patient’s diagnosis, risk factors, and predicted response to treatment [15]. This approach has the potential to optimise treatment selection and minimise inefficient or ineffective therapies.

Challenges and Future Directions

While the applications of AI in LBP algorithms are promising, several challenges need to be addressed:

1. Data Quality and Quantity: High-quality, large-scale datasets are essential for training reliable AI models. Medical datasets can be noisy, incomplete, or biased, affecting the performance of AI algorithms [16]. Standardisation of data collection procedures is needed.
2. Algorithm Robustness and Generalisability: AI models trained on one dataset may not generalise well to other populations or clinical settings [17]. External validation and careful model selection are critical to ensure robustness. 
3. Explainability: Many AI models, particularly deep learning networks, are “black boxes,” making it difficult to interpret the specific factors driving their predictions. Transparency and explainability are important for building trust and clinician acceptance.
4. Ethical Considerations: The use of AI in healthcare raises ethical concerns related to bias in algorithms, data privacy, and the potential for dehumanisation of care. Careful ethical frameworks are needed.
5. Implementation and Integration: Integrating AI algorithms into existing clinical workflows requires overcoming practical challenges related to technical infrastructure, user training, and regulatory approvals [18].

Future research should focus on enhancing the robustness, generalisability, and explainability of AI models for LBP algorithms, while focusing on data quality and implementing robust validation.

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of M3 India.

About the author of this article: Dr Partha Ghosh, BNYS, MD(YS), is a general physician and a medical writer from Siliguri, Darjeeling.