Use of artificial neural networks to identify the predictive factors of extracorporeal shock wave therapy treating patients with chronic plantar fasciitis

Predictive Prognostic Factors in Non-Calcific Supraspinatus Tendinopathy Treated with Focused Extracorporeal Shock Wave Therapy: An Artificial Neural Network Approach

The supraspinatus tendon is one of the most involved tendons in the development of shoulder pain. Extracorporeal shockwave therapy (ESWT) has been recognized as a valid and safe treatment. Sometimes the symptoms cannot be relieved, or a relapse develops, affecting the patient's quality of life. Therefore, a prediction protocol could be a powerful tool aiding our clinical decisions. An artificial neural network was run, in particular a multilayer perceptron model incorporating input information such as the VAS and Constant-Murley score, administered at T0 and at T1 after six months. It showed a model sensitivity of 80.7%, and the area under the ROC curve was 0.701, which demonstrates good discrimination. The aim of our study was to identify predictive factors for minimal clinically successful therapy (MCST), defined as a reduction of ≥40% in VAS score at T1 following ESWT for chronic non-calcific supraspinatus tendinopathy (SNCCT). From the male gender, we expect greater and more frequent clinical success. The more severe the patient's initial condition, the greater the possibility that clinical success will decrease. The Constant and Murley score, Roles and Maudsley score, and VAS are not just evaluation tools to verify an improvement; they are also prognostic factors to be taken into consideration in the assessment of achieving clinical success. Due to the lower clinical improvement observed in older patients and those with worse clinical and functional scales, it would be preferable to also provide these patients with the possibility of combined treatments. The ANN predictive model is reasonable and accurate in studying the influence of prognostic factors and achieving clinical success in patients with chronic non-calcific tendinopathy of the supraspinatus treated with ESWT.

Treatment Algorithm for the Resorption of Calcific Tendinitis Using Extracorporeal Shockwave Therapy: A Data Mining Study

Background

Although evidence indicates that extracorporeal shockwave therapy (ESWT) is effective in treating calcifying shoulder tendinitis, incomplete resorption and dissatisfactory results are still reported in many cases. Data mining techniques have been applied in health care in the past decade to predict outcomes of disease and treatment.

Purpose

To identify the ideal data mining technique for the prediction of ESWT-induced shoulder calcification resorption and the most accurate algorithm for use in the clinical setting.

Study Design

Case-control study.

Methods

Patients with painful calcified shoulder tendinitis treated by ESWT were enrolled. Seven clinical factors related to shoulder calcification were adopted as the input attributes: sex, age, side affected, symptom duration, pretreatment Constant-Murley score, and calcification size and type. The 5 data mining techniques assessed were multilayer perceptron (neural network), naïve Bayes, sequential minimal optimization, logistic regression, and the J48 decision tree classifier.

Results

A total of 248 patients with calcified shoulder tendinitis were enrolled in this study. Shorter symptom duration yielded the highest gain ratio (0.374), followed by smaller calcification size (0.336) and calcification type (0.253). With the J48 decision tree method, the accuracy of 3 input attributes was 89.5% by 10-fold cross-validation, indicating satisfactory accuracy. A treatment algorithm using the J48 decision tree indicated that a symptom duration of ≤10 months was the most positive indicator of calcification resorption, followed by a calcification size of ≤10.82 mm.

Conclusion

The J48 decision tree method demonstrated the highest precision and accuracy in the prediction of shoulder calcification resorption by ESWT. A symptom duration of ≤10 months or calcification size of ≤10.82 mm represented the clinical scenarios most likely to show resorption after ESWT.

Predicting the outcome of plantar heel pain in adults: a systematic review of prognostic factors

BACKGROUND

Plantar Heel Pain (PHP) is a common disorder with many treatment pathways and is not self-limiting, hence prognostic information concerning recovery or recalcitrance is needed to guide practice. In this systematic review, we investigate which prognostic factors are associated with favourable or unfavourable PHP outcomes.

METHODS

MEDLINE, Web of Science, EMBASE, Scopus and PubMed electronic bibliographic databases were searched for studies evaluating baseline patient characteristics associated with outcomes in prospective longitudinal cohorts or after specific interventions. Cohort, clinical prediction rule derivation and single arms of randomised controlled trials were included. Risk of bias was evaluated with method-specific tools and evidence certainty with GRADE.

RESULTS

The review included five studies which evaluated 98 variables in 811 participants. Prognostic factors could be categorised as demographics, pain, physical and activity-related. Three factors including sex and bilateral symptoms (HR: 0.49[0.30-0.80], 0.33[0.15-0.72], respectively) were associated with a poor outcome in a single cohort study. The remaining four studies reported twenty factors associated with a favourable outcome following shockwave therapy, anti-pronation taping and orthoses. Heel spur (AUC = 0.88[0.82-0.93]), ankle plantar-flexor strength (Likelihood ratio (LR): 2.17[1.20-3.95]) and response to taping (LR = 2.17[1.19-3.90]) were the strongest factors predicting medium-term improvement. Overall, the study quality was low. A gap map analysis revealed an absence of research that included psychosocial factors.

CONCLUSIONS

A limited number of biomedical factors predict favourable or unfavourable PHP outcomes. High quality, adequately powered, prospective studies are required to better understand PHP recovery and should evaluate the prognostic value of a wide range of variables, including psychosocial factors.

Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review

Background

There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation).

Methods

A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)' performance, and validity (internal or external).

Results

A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as "other." Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation.

Conclusion

Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications.

Level of Evidence

Level III, retrospective cohort study.

Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations

Large variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. It may also support the success of novel drug development in pain, making it easier to identify novel treatments that work for certain patients and more accurately identify the magnitude of the treatment effect for those subgroups. Significant preliminary work has been done in this area, and analgesic trials are beginning to utilize precision pain medicine approaches such as stratified allocation on the basis of prespecified patient phenotypes using assessment methodologies such as quantitative sensory testing. Current major challenges within the field include: 1) identifying optimal measurement approaches to assessing patient characteristics that are most robustly and consistently predictive of inter-patient variation in specific analgesic treatment outcomes, 2) designing clinical trials that can identify treatment-by-phenotype interactions, and 3) selecting the most promising therapeutics to be tested in this way. This review surveys the current state of precision pain medicine, with a focus on drug treatments (which have been most-studied in a precision pain medicine context). It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.

Use of artificial neural networks in the prognosis of musculoskeletal diseases-a scoping review

To determine the current evidence on artificial neural network (ANN) in prognostic studies of musculoskeletal diseases (MSD) and to assess the accuracy of ANN in predicting the prognosis of patients with MSD. The scoping review was reported under the Preferred Items for Systematic Reviews and the Meta-Analyses extension for Scope Reviews (PRISMA-ScR). Cochrane Library, Embase, Pubmed, and Web of science core collection were searched from inception to January 2023. Studies were eligible if they used ANN to make predictions about MSD prognosis. Variables, model prediction accuracy, and disease type used in the ANN model were extracted and charted, then presented as a table along with narrative synthesis. Eighteen Studies were included in this scoping review, with 16 different types of musculoskeletal diseases. The accuracy of the ANN model predictions ranged from 0.542 to 0.947. ANN models were more accurate compared to traditional logistic regression models. This scoping review suggests that ANN can predict the prognosis of musculoskeletal diseases, which has the potential to be applied to different types of MSD.

CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach

The purpose of our study is to predict the occurrence and prognosis of diabetic foot ulcers (DFUs) by clinical and lower extremity computed tomography angiography (CTA) data of patients using the artificial neural networks (ANN) model. DFU is a common complication of diabetes that severely affects the quality of life of patients, leading to amputation and even death. There are a lack of valid predictive techniques for the prognosis of DFU. In clinical practice, the use of scales alone has a large subjective component, leading to significant bias and heterogeneity. Currently, there is a lack of evidence-based support for patients to develop clinical strategies before reaching end-stage outcomes. The present study provides a novel technical tool for predicting the prognosis of DFU. After screening the data, 203 patients with diabetic foot ulcers (DFUs) were analyzed and divided into two subgroups based on their Wagner Score (138 patients in the low Wagner Score group and 65 patients in the high Wagner Score group). Based on clinical and lower extremity CTA data, 10 predictive factors were selected for inclusion in the model. The total dataset was randomly divided into the training sample, testing sample and holdout sample in ratio of 3:1:1. After the training sample and testing sample developing the ANN model, the holdout sample was utilized to assess the accuracy of the model. ANN model analysis shows that the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of the overall ANN model were 92.3%, 93.5%, 87.0%, 94.2% and 0.955, respectively. We observed that the proposed model performed superbly on the prediction of DFU with a 91.6% accuracy. Evaluated with the holdout sample, the model accuracy, sensitivity, specificity, PPV and NPV were 88.9%, 90.0%, 88.5%, 75.0% and 95.8%, respectively. By contrast, the logistic regression model was inferior to the ANN model. The ANN model can accurately and reliably predict the occurrence and prognosis of a DFU according to clinical and lower extremity CTA data. We provided clinicians with a novel technical tool to develop clinical strategies before end-stage outcomes.

The role of AI technology in prediction, diagnosis and treatment of colorectal cancer

Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.

Artificial intelligence in colorectal cancer management

Artificial intelligence (AI) is a new branch of computer science involving many disciplines and technologies. Since its application in the medical field, it has been constantly studied and developed. AI includes machine learning and neural networks to create new technologies or to improve existing ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer (CRC). This mini-review aims to summarize the progress of research and possible clinical applications of AI in the investigation, early diagnosis, treatment, and management of CRC, to offer elements of knowledge as a starting point for new studies and future applications.

Development and Validation of a Novel Scoring System for Severity of Plantar Fasciitis

OBJECTIVES

Plantar fasciitis (PF) is the most common cause of heel pain. Though PF is self-limited, it can develop into chronic pain and thus treatment is needed. Early and accurate prognostic assessment of patients with PF is critically important for selecting the optimal treatment pathway. Nevertheless, there is no scoring system to determine the severity of PF and no prognostic model in choosing between conservative or surgical treatment. The study aimed to develop a novel scoring system to evaluate the severity of plantar fasciitis and predict the prognosis of conservative treatment.

METHODS

Data of consecutive patients treated from 2014 to 2018 were retrospectively collected. One hundred and eighty patients were eligible for the study. The demographics and clinical characteristics served as independent variables. The least follow-up time was 6 months. A minimal reduction of 60% in the visual analog scale (VAS) score from baseline was considered as minimal clinically important difference (MCID). Those factors significantly associated with achieving MCID in univariate analyses were further analyzed by multivariate logistic regression. A novel scoring system was developed using the best available literature and expert-opinion consensus. Inter-observer reliability and intra-observer reproducibility were evaluated. The appropriate cut-off points for the novel score system were obtained using receiver operating characteristic (ROC) curves.

RESULTS

The system score = VAS (0-3 point = 1; 3.1-7 point = 3; 7.1-10 point = 5) + duration of symptoms (<6 months = 1; ≥1 6 months = 2) + ability to walk without pain (>1 h = 1; ≤1 h = 4) + heel spur in X-ray (No = 0; Yes = 2) + high intensity zone (HIZ) in MRI (No = 0; Yes = 2). The total score was divided in four categories of severity: mild (2-4 points), moderate (5-8 points), severe (9-12 points), and critical (13-15 points). Inter-observer agreement with a value of 0.84 was considered as perfect reliability. Intra-observer reproducibility with a value of 0.92 was considered as perfect reproducibility. The optimum cut-off value was 10 points. The sensitivity of predictive factors was 86.37%, 84.21%, 91.22%, 84.12%, and 89.32%, respectively; the specificity was 64.21%, 53.27%, 67.76%, 62.37%, and 79.58%, respectively; the area under curve was 0.75, 0.71, 0.72, 0.87, and 0.77, respectively. The Hosmer-Lemeshow test showed a good fitting of the score system with an overall accuracy of 90.6%.

CONCLUSIONS

Based on prognostic factors, the present study establishes a novel scoring system which is highly comprehensible, reliable, and reproducible. This score system can be used to identify the severity of plantar fasciitis and predict the prognosis of conservative treatment accurately. The application of this scoring system in clinical settings can significantly improve the decision-making process.

Extracorporeal Shockwave Therapy Treatment in Upper Limb Diseases: A Systematic Review

Background: Rotator cuff tendinopathy (RCT), subacromial impingement (SAIS), and medial (MEP) and lateral (LEP) epicondylitis are the most common causes of upper limb pain caused by microtrauma and degeneration. There are several therapeutic choices to manage these disorders: extracorporeal shockwave therapy (ESWT) has become a valuable option.

METHODS

A systematic review of two electronic medical databases was performed by two independent authors, using the following inclusion criteria: RCT, SAIS, MEP, and LEP, ESWT therapy without surgical treatment, with symptoms duration more than 2 months, and at least 6 months of follow-up. Studies of any level of evidence, reporting clinical results, and dealing with ESWT therapy and RCT, SAIS, MEP, and LEP were included.

RESULTS

A total of 822 articles were found. At the end of the first screening, following the previously described selection criteria, we selected 186 articles eligible for full-text reading. Ultimately, after full-text reading, and reference list check, we selected 26 articles following previously written criteria.

CONCLUSIONS

ESWT is a safe and effective treatment of soft tissue diseases of the upper limbs. Even in the minority cases when unsatisfied results were recorded, high energy shockwaves were nevertheless suggested in prevision of surgical treatment.