Artificial intelligence's role in vascular surgery decision-making

Humans use tools: From handcrafted tools to artificial intelligence

Human evolution is instrument based. Humans created tools >2 million years ago to aid them in hunting, gathering, and defense, allowing them to build shelters and farms and transport goods and people over great distances. Written records preserved our knowledge and experiences for future generations. Instruments have greatly influenced surgery. Knives and needles were used by ancient surgeons, whereas lasers, endoscopes, and robotics are used today. Artificial intelligence (AI) is the future of surgical instruments, increasing precision through self-evaluation, but development remains in the early stages. Vascular surgery research and practice has used AI-powered systems that can track patient progress and identify vascular disease risk using deep learning and pattern recognition, as well as improved radiological interpretation of vascular imaging and medicine. Using insights and data-driven recommendations, AI-powered decision support systems could help surgeons in enhancing patient outcomes by providing guidance to navigate complex anatomy and identify anomalies. Robots can assist surgeons in performing risky, complex operations with optimal outcomes. Human expertise and AI will revolutionize surgery, enhancing its safety, precision, and efficacy. Surgical applications of AI raise numerous questions and debates. Data must be representative of all populations, data management must protect the privacy of patients and physicians, and the AI decision-making process must be clarified to produce validated models that can be used ethically. Vascular surgeons' judgment and experience should not be automated. Instead, AI should contribute to the efficiency and effectiveness of vascular surgeons. Human clinicians must interpret AI-generated data, use clinical judgment, and build empathy, compassion, and shared decision-making to sustain doctor-patient relationships. From simple tools to complex modern technologies, the history of tools reveals human creativity. Our environment has been altered by technology, ensuring our survival and growth. AI is still a half-told tale that will inspire and amaze us for years to come.

Mature artificial intelligence- and machine learning-enabled medical tools impacting vascular surgical care: A scoping review of late-stage, US Food and Drug Administration-approved or cleared technologies relevant to vascular surgeons

Artificial intelligence and machine learning (AI/ML)-enabled tools are shifting from theoretical or research-only applications to mature, clinically useful tools. The goal of this article was to provide a scoping review of the most mature AI/ML-enabled technologies reviewed and cleared by the US Food and Drug Administration relevant to the field of vascular surgery. Despite decades of slow progress, this landscape is now evolving rapidly, with more than 100 AI/ML-powered tools being approved by the US Food and Drug Administration each year. Within the field of vascular surgery specifically, this review identified 17 companies with mature technologies that have at least one US Food and Drug Administration clearance, all occurring between 2016 and 2022. The maturation of these technologies appears to be accelerating, with improving regulatory clarity and clinical uptake. The early AI/ML-powered devices extend or amplify clinically entrenched platform technologies and tend to be focused on the diagnosis or evaluation of time-sensitive, clinically important pathologies (eg, reading Digital Imaging and Communications in Medicine-compliant computed tomography images to identify pulmonary embolism), or when physician efficiency or time savings is improved (eg, preoperative planning and intraoperative guidance). The majority (>75%) of these technologies are at the intersection of radiology and vascular surgery. It is becoming increasingly important that the contemporary vascular surgeon understands this shifting paradigm, as these once-nascent technologies are finally maturing and will be encountered with increasingly regularity in daily clinical practice.

A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay

INTRODUCTION

Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS).

METHODS

Data was collected from the trauma registry at an urban level one adult trauma center and included patients from 1/1/2014 to 3/31/2019. Trauma patients with one or fewer days LOS were excluded. Single layer and deep artificial neural networks were trained to identify patients in the top quartile of LOS and optimized on area under the receiver operator characteristic curve (AUROC). The predictive performance of the model was assessed on a separate test set using binary classification measures of accuracy, precision, and error.

RESULTS

2953 admitted trauma patients with more than one-day LOS were included in this study. They were 70% male, 60% white, and averaged 47 years-old (SD: 21). 28% were penetrating trauma. Median length of stay was 5 days (IQR 3-9). For prediction of prolonged LOS, the deep neural network achieved an AUROC of 0.80 (95% CI: 0.786-0.814) specificity was 0.95, sensitivity was 0.32, with an overall accuracy of 0.79.

CONCLUSION

Machine learning can predict, with excellent specificity, trauma patients who will have prolonged length of stay with only physiologic and demographic data available at the time of admission. These patients may benefit from additional resources with respect to disposition planning at the time of admission.

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.