Musculoskeletal Ultrasound Image-Based Radiomics for the Diagnosis of Achilles Tendinopathy in Skiers

A Multiplanar Radiomics Model Based on Cranial Ultrasound to Predict the White Matter Injury in Premature Infants and an Analysis of its Correlation With Neurodevelopment

OBJECTIVES

To develop and evaluate a multiplanar radiomics model based on cranial ultrasound (CUS) to predict white matter injury (WMI) in premature infants and explore its correlation with neurodevelopment.

METHODS

We retrospectively reviewed 267 premature infants. The radiomics features were extracted from five standard sections of CUS. The Spearman's correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was applied to select features and build radiomics signature, and a multiplanar radiomics model was constructed based on the radiomics signature of five planes. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). Infants with WMI were re-examined by ultrasound at 2 and 4 weeks after birth, and the recovery degree of WMI was evaluated using multiplanar radiomics. The relationship between WMI and the recovery degree and neurodevelopment was analyzed.

RESULTS

The AUC of the multiplanar radiomics in the training and validation sets were 0.94 and 0.91, respectively. The neurodevelopmental function scores in infants with WMI were significantly lower than those in healthy preterm infants and full-term newborns (P < .001). There were statistically significant differences in the neurodevelopmental function scores of infants between the 2- and 4-week lesion disappearance and 4-week lesion persistence (P < .001).

CONCLUSIONS

The multiplanar radiomics model showed a good performance in predicting the WMI of premature infants. It can not only provide objective and accurate results but also dynamically monitor the degree of recovery of WMI to predict the prognosis of premature infants.

The Future of Artificial Intelligence in Sports Medicine and Return to Play

Artificial intelligence (AI) has shown tremendous growth over the last decade, with the more recent development of clinical applications in health care. The ability of AI to synthesize large amounts of complex data automatically allows health care providers to access previously unavailable metrics and thus enhance and personalize patient care. These innovations include AI-assisted diagnostic tools, prediction models for each treatment pathway, and various tools for workflow optimization. The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play.

Artificial intelligence in foot and ankle surgery: current concepts

The twenty-first century has proven that data are the new gold. Artificial intelligence (AI) driven technologies might potentially change the clinical practice in all medical specialities, including orthopedic surgery. AI has a broad spectrum of subcomponents, including machine learning, which consists of a subdivision called deep learning. AI has the potential to increase healthcare delivery, improve indications and interventions, and minimize errors. In orthopedic surgery. AI supports the surgeon in the evaluation of radiological images, training of surgical residents, and excellent performance of machine-assisted surgery. The AI algorithms improve the administrative and management processes of hospitals and clinics, electronic healthcare databases, monitoring the outcomes, and safety controls. AI models are being developed in nearly all orthopedic subspecialties, including arthroscopy, arthroplasty, tumor, spinal and pediatric surgery. The present study discusses current applications, limitations, and future prospective of AI in foot and ankle surgery.

Evaluating the Quality and Usability of Artificial Intelligence-Generated Responses to Common Patient Questions in Foot and Ankle Surgery

Background

Artificial intelligence (AI) platforms, such as ChatGPT, have become increasingly popular outlets for the consumption and distribution of health care-related advice. Because of a lack of regulation and oversight, the reliability of health care-related responses has become a topic of controversy in the medical community. To date, no study has explored the quality of AI-derived information as it relates to common foot and ankle pathologies. This study aims to assess the quality and educational benefit of ChatGPT responses to common foot and ankle-related questions.

Methods

ChatGPT was asked a series of 5 questions, including "What is the optimal treatment for ankle arthritis?" "How should I decide on ankle arthroplasty versus ankle arthrodesis?" "Do I need surgery for Jones fracture?" "How can I prevent Charcot arthropathy?" and "Do I need to see a doctor for my ankle sprain?" Five responses (1 per each question) were included after applying the exclusion criteria. The content was graded using DISCERN (a well-validated informational analysis tool) and AIRM (a self-designed tool for exercise evaluation).

Results

Health care professionals graded the ChatGPT-generated responses as bottom tier 4.5% of the time, middle tier 27.3% of the time, and top tier 68.2% of the time.

Conclusion

Although ChatGPT and other related AI platforms have become a popular means for medical information distribution, the educational value of the AI-generated responses related to foot and ankle pathologies was variable. With 4.5% of responses receiving a bottom-tier rating, 27.3% of responses receiving a middle-tier rating, and 68.2% of responses receiving a top-tier rating, health care professionals should be aware of the high viewership of variable-quality content easily accessible on ChatGPT.

Level of Evidence

Level III, cross sectional study.

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.