Aim: Images in different laparoscopic cholecystectomy datasets are acquired using various camera models, parameters, and settings, with the annotation methods varying by institution. These factors result in inconsistent inference performance of the network model. This study aims to identify the optimal network model architecture for liver and gallbladder segmentation from several options. Then, the performance and robustness of the optimal network model are evaluated using an independent dataset that is not included in the training.
Methods: The public dataset, CholecSeg8k, was utilized as the input for the network model training, validation, and testing. A local private dataset from KPJ Damansara Hospital, Selangor, Malaysia, was used for testing purposes only. For the implementation of liver and gallbladder segmentation, segmentation models, a public Python library was employed.
Results: Among the experiments, highly accurate liver and gallbladder segmentation results were achieved using the feature pyramid network (FPN) architecture as the network model, with the Inception-ResNet-v2 architecture as the network backbone. The best-trained network model resulted in a loss of 0.070955, a mean intersection over union (IoU) score of 0.95896, and a mean F1-score of 0.9773 on the test set. However, visualized results for the private dataset contained considerable false-negative areas.
Conclusion: The proposed automated technique has the potential to serve as an alternative to the conventional indocyanine green injection along with near-infrared fluorescence imaging (ICG-NIRF)-based method for liver and gallbladder segmentation during laparoscopic cholecystectomy. Future work will focus on enhancing the results of the private dataset. Additionally, a surgeon-assistant robotic arm that will use the liver and gallbladder segmentation results for camera steering will be analyzed.
The field of spine surgery has long been characterized by innovations and technological advancements. The integration of artificial intelligence (AI) into spine surgery represents one of the latest technical developments in the field. The ability of AI to rapidly analyze datasets improves decision making, risk assessment, intraoperative precision, and postoperative management, all of which contribute to increasing personalized spine care and improving outcomes. However, the successful implementation of AI faces regulatory and privacy challenges that must be addressed before its full potential can be realized. Here, we provide a detailed analysis of the current applications and future prospects of AI in spine surgery, highlighting both the opportunities and challenges in this evolving field.
Artificial intelligence (AI) and machine learning (ML) involve the usage of complex algorithms to identify patterns, predict future outcomes, generate new data, and perform other tasks that typically require human intelligence. AI tools have been progressively adopted by multiple disciplines of surgery, enabling increasingly patient-specific care, as well as more precise surgical modeling and assessment. For instance, AI tools such as ChatGPT have been applied to enhance both patient educational materials and patient-surgeon communication. Additionally, AI tools have helped support pre- and postoperative assessment in a diverse set of procedures, including breast reconstructions, facial surgeries, hand surgeries, wound healing operations, and burn surgeries. Further, ML-supported 3D modeling has now been utilized for patient-specific surgical planning and may also be combined with 3D printing technologies to generate patient-customized, implantable constructs. Ultimately, the advent of AI and its intersection with surgical practice have demonstrated immense potential to transform patient care by making multiple facets of the surgical process more efficient, precise, and patient-specific.
Aim: The purpose of this study was to determine the quality and accessibility of the outputs from a healthcare-specific artificial intelligence (AI) platform for common questions during the perioperative period for a common plastic surgery procedure.
Methods: Doximity GPT (Doximity, San Francisco, CA) and ChatGPT 3.5 (OpenAI, San Francisco, CA) were utilized to search 20 common perioperative patient inquiries regarding breast augmentation. The structure, content, and readability of responses were compared using t-tests and chi-square tests, with P < 0.05 used as the cutoff for significance.
Results: Out of 80 total AI-generated outputs, ChatGPT responses were significantly longer (331 vs. 218 words, P < 0.001). Doximity GPT outputs were structured as a letter from a medical provider to the patient, whereas ChatGPT outputs were a bulleted list. Doximity GPT outputs were significantly more readable by four validated scales: Flesch Kincaid Reading Ease (42.6 vs. 29.9, P < 0.001) and Flesch Kincaid Grade Level (11.4 vs. 14.1 grade, P < 0.001), Coleman-Liau Index (14.9 vs. 17 grade, P < 0.001), and Automated Readability Index (11.3 vs. 14.8 grade, P < 0.001). Regarding content, there was no difference between the two platforms regarding the appropriateness of the topic (99% overall). Medical advice from all outputs was deemed reasonable.
Conclusion: Doximity’s AI platform produces reasonable, accurate information in response to common patient queries. With continued reinforcement learning with human feedback (RLHF), Doximity GPT has the potential to be a useful tool to plastic surgeons and can assist with a range of tasks, such as providing basic information on procedures and writing appeal letters to insurance providers.
The integration of artificial intelligence (AI) into spine surgery presents a transformative approach to preoperative and postoperative care paradigms. This paper explores the application of AI within spine surgery, focusing on diagnostic and predictive applications. AI-driven analysis of radiographic images, facilitated by machine learning (ML) algorithms such as convolutional neural networks (CNNs), potentially promises enhanced accuracy in identifying spinal pathologies and deformities; by combining these techniques with patient-specific data, predictive modeling can guide and inform diagnosis, prognosis, surgery selection, and treatment. Postoperatively, AI can leverage data from digital wearable technology to enhance the quantity and quality of outcome measures surgeons use to define and understand treatment success or failure. Still, challenges such as internal and external validation of AI models remain pertinent. Future directions include incorporating continuous variables from digital biomarkers and standardizing reporting metrics for AI studies in spine surgery. As AI continues to evolve, transparent validation frameworks and adherence to reporting guidelines will be crucial for its successful integration into clinical practice.
Aim: This article aims to explore how three dimensions (3D) virtual modeling enhances accuracy and efficiency in detecting hepatic metastases, with a specific focus on “vanishing tumors” that are difficult to detect using traditional imaging techniques. It also aims to demonstrate the potential impact of these advanced technologies on improving diagnostic and treatment strategies for patients with liver cancer.
Methods: Eight patients with liver metastases from colorectal cancer were studied using magnetic resonance imaging (MRI) and 3D virtual modeling to enhance surgical planning by accurately locating lesions. The concordance between these imaging techniques and the Gold Standard was assessed using Gwet’s AC1 coefficient, with statistical analysis performed through resampling methods and non-parametric tests due to non-normal AC1 distribution. Liver segmentation was conducted semi-automatically using IntelliSpace Portal (ISP). The study’s evaluation model involved questionnaires for medical professionals across four cohorts, aiming to determine the 3D model’s effectiveness in identifying lesion locations for surgery.
Results: This study investigated the efficacy of 3D virtual modeling in identifying hepatic metastases, particularly comparing its accuracy with traditional MRI in locating lesions. The findings indicate that MRI generally provided better concordance with the Gold Standard for lesion localization, except for a few experienced users who had prior familiarity with the 3D models. Despite the mixed results in accuracy, the study suggests potential benefits of 3D modeling in enhancing surgical planning and execution, particularly in detecting “vanishing” liver metastases that are difficult to visualize with standard imaging techniques. The research aligns with broader evidence indicating the utility of 3D models in improving outcomes in hepatic surgery by enabling more precise resections and reducing postoperative complications. However, the study also notes the challenge of quantifying the added value of 3D modeling due to the unique nature of each surgical case and the potential bias in the user experience with the technology.
Conclusion: The conclusion of this study underscores the significant potential of 3D virtual modeling in enhancing the precision of locating and resecting hepatic metastases, particularly those elusive “vanishing tumors”. This research underscores the value of integrating advanced 3D imaging techniques into surgical practice, suggesting a paradigm shift toward more accurate and less invasive liver surgeries. Future studies should aim to further quantify the benefits of 3D modeling in surgical outcomes, investigate its utility across different surgical experiences, and explore the integration of artificial intelligence (AI) to maximize its effectiveness in clinical settings.
AI is revolutionizing the landscape of colorectal cancer (CRC) surgery, permeating diverse facets ranging from intraoperative guidance to predictive modeling of postoperative outcomes. This scoping review aims to comprehensively delineate the breadth of artificial intelligence (AI) applications in CRC surgery. A search of PubMed, Embase, and Ebsco databases up to December 2023 was conducted, with registration in the international prospective register of systematic reviews (PROSPERO) (CRD42024502107). Sixty-two studies meeting stringent inclusion criteria were scrutinized, encompassing AI utilization in CRC surgery or the development of AI-driven tools for colorectal surgical practice. Five principal domains of AI application emerged: (i) Intraoperative guidance, leveraging real-time navigation, indocyanine green (ICG) angiography, and hyperspectral imaging (HSI) to enhance surgical precision; (ii) Image segmentation, facilitating phase recognition, tools recognition, and anatomical identification to optimize surgical visualization; (iii) Training and performance assessment, enabling objective evaluation and enhancement of surgical skills through AI-driven simulations and feedback mechanisms; (iv) Prediction of surgical complications, encompassing prognostication of anastomotic leakage (AL) or stricture, stoma requirements, and prediction of low anterior resection syndrome (LARS) and short-term postoperative complications; (v) Utilization of electronic health records (EHRs), harnessing AI algorithms to streamline data analysis and inform decision-making processes. This review underscores the paradigm-shifting impact of AI in CRC surgery, transcending conventional boundaries and catalyzing advancements across diverse surgical domains. Although many applications are still experimental, as AI continues to evolve, it promises to transform surgical practice, optimize outcomes, and revolutionize patient care. Embracing AI technologies is imperative for colorectal surgeons to remain at the vanguard of surgical innovation and deliver superior outcomes for CRC patients.
Our aging population, diabetes, and obesity have fueled the growth of chronic wounds seen throughout the world. Often, wounds are a marker of poor health that leads to increased mortality rates. However, the diagnosis and treatment of these wounds are challenging. Incorrectly differentiating between chronic wounds and other complex conditions can lead to adverse events. Artificial intelligence (AI) has been shown to offer some early benefits, and we hypothesized that it may enhance wound care but also carry some notable risks. We performed a detailed search using PubMed, Scopus, Cumulated Index in Nursing and Allied Health Literature, and Web of Science for AI applications in wound care. AI was found to be applied to wound diagnosis and characterization, wound monitoring for tissue change, daily therapy, and prevention and prognostics. AI made for more efficient and accurate wound assessments, less painful assessments of chronic wounds, more personalized treatment, and improved prognostic prediction capabilities. AI also allowed for more precise at-home observation and care, facilitating earlier wound treatment as needed. Challenges associated with AI included how to best allocate AI-assisted technologies equitably, how to safely maintain patient data, and how to diversify datasets for algorithm training. Because the algorithms are not transparent, validating findings may be challenging. AI presents a powerful tool in several aspects of advanced wound care and has the potential to improve diagnoses, accelerate healing, reduce pain, and improve the cost-effectiveness of wound care. More research needs to be done into how to best incorporate AI into daily clinical practice while keeping clinicians aware of the potential risks of using these evolving technologies.
This review explores the current applications, benefits, and challenges of artificial intelligence (AI) in plastic, reconstructive, and aesthetic surgery. In recent years, AI has found its way into everyday life, including the healthcare sector. To deepen the understanding of the use and handling of AI in plastic and reconstructive surgery, this review provides valuable insights into modern practices, illustrated with real examples and potential future applications. While the advantages of AI are obvious, the disadvantages cannot be ignored. This review aims to highlight possible risks, dangers, and sources of error inherent in AI itself and its applications. Therefore, this paper seeks to address possible concerns and questions about AI in plastic surgery while offering a realistically neutral insight. Additionally, fundamental ethical and legal principles will be discussed, as well as possible “rules of the game” for the application and integration of AI in surgery. Innovations in this field are often hailed as miracles, making it crucial to evaluate them critically and objectively. Although progress in AI cannot and should not be halted, it is important to strengthen the trained approach and always look at the whole picture.
Artificial intelligence (AI), deep learning (DL), and machine learning (ML) algorithms are revolutionizing spine surgery. Soon, these technologies may allow the integration of automated devices into clinical practice. The roles of such devices are yet to be imagined and then developed, but one could assume that automated surgical devices can assist spine surgeons in a variety of ways, such as contextual guidance, precise screw placements, or intraoperative monitoring. In the not-too-distant future, such devices may be able to perform entire surgeries autonomously. Current literature suggests that advancements toward autonomous robotic surgery may improve surgical approaches and reduce negative clinical variation in spine surgery outcomes. This review aims to examine the current trends, practices, and advancements in surgical automation and provide an overview of the stages of automation of devices currently employed within spine surgery.
Aim: The purpose of this study is to investigate the utility of incorporating magnetic resonance imaging (MRI) into an artificial intelligence (AI) model to preoperatively predict pseudarthrosis for patients undergoing adult spinal deformity (ASD) surgery.
Methods: A retrospective cohort study was conducted on patients undergoing ASD surgery at Vanderbilt University Medical Center with at least 2 years of follow-up. We first collected demographic variables and measured traditional radiographic variables with Surgimap software. The primary outcome of interest was pseudarthrosis, defined as mechanical pain without evidence of bony union with or without a rod fracture. Next, cohort differences between patients diagnosed with and without pseudarthrosis were evaluated with t-tests for continuous variables and chi-squared tests for categorical variables using Bonferroni-Holm multiple comparison correction. Using a subpopulation of patients with preoperative thoracic MRI available, a three-dimensional convolutional neural network (3D-CNN) with five-fold nested cross-validation was developed to predict pseudarthrosis - accuracy was evaluated with the Youden index. Finally, class activation mapping (CAM) was conducted to visualize the MRI features utilized by the model for accurate classifications.
Results: Of 191 patients undergoing ASD surgery, the demographic and traditional radiographic variables were collected, and only age was observed to be significantly different between the patients diagnosed with pseudarthrosis (69.9 ± 10.1 years old) and those without (60.9 ± 19.9), with a t-test P-value of 0.003. The 3D-CNN demonstrated an average Youden index of 0.49 ± 0.25 on the withheld data, with a P-value of 5.50e-3 compared to an equivocal null model. Finally, CAM consistently revealed posterior adipose tissue to be most important in preoperatively predicting pseudarthrosis.
Conclusion: Adipose tissue features in MRI, independent of body mass index (BMI), may be useful for preoperatively predicting pseudarthrosis. Overall, this work demonstrates the capabilities of raw imaging AI in spine surgery and can serve as the basis for a deeper biological inquiry into the pathogenesis of pseudarthrosis.
Aim: This study aims to investigate the ability of ChatGPT to answer common patient questions related to the five most common aesthetic procedures.
Methods: We asked three questions, two of which were the same across procedures, for each of the five most common plastic surgery procedures as determined by the 2022 American Society of Plastic Surgery (ASPS) Procedural Statistics Release. These were posed to ChatGPT on the same day, using the same account. Then, they were compared to the corresponding information on the ASPS website.
Results: We found that ChatGPT provides accurate, organized, and grammatically correct responses to common patient questions. It was comparable to the ASPS website in terms of comprehensiveness of the complications listed across procedures. However, its responses regarding recovery time were less detailed than the corresponding ASPS articles. It included accurate information on recovery time that was unavailable on the ASPS site. For procedure-specific questions, ChatGPT was more detailed 2/5 times, less detailed 1/5 times, and provided a completely different answer than the ASPS website 2/5 times.
Conclusion: This study provides support for ChatGPT’s utilization as a tool to improve the efficiency of consultations for aesthetic procedures. However, it is important to recognize ChatGPT’s limitations in answering questions in a patient/procedure-specific way. Therefore, it is not a substitute for an experienced surgeon consultation. Further research is needed to assess the reliability of ChatGPT before it can be fully recommended as an ultimate patient learning tool.
The volume and complexity of clinical data are growing rapidly. The potential for artificial intelligence (AI) and machine learning (ML) to significantly impact plastic and craniofacial surgery is immense. This manuscript reviews the overall landscape of AI in craniofacial surgery, highlighting the scarcity of prospective and clinically translated models. It examines the numerous clinical promises and challenges associated with AI, such as the lack of robust legislation and structured frameworks for its integration into medicine. Clinical translation considerations are discussed, including the importance of ensuring clinical utility for real-world use. Finally, this commentary brings forward how clinicians can build trust and sustainability toward model-driven clinical care.