Towards a Low-Cost Monitor-Based Augmented Reality Training Platform for At-Home Ultrasound Skill Development

Innovations in ultrasound training in obstetrics

INTRODUCTION

Ultrasound technology is critical in obstetrics, enabling detailed examination of the fetus and maternal anatomy. However, increasing complexity demands specialised training to maximise its potential. This study explores innovative approaches to ultrasound training in obstetrics, focussing on enhancing diagnostic skills and patient safety.

METHODS

This review examines recent innovations in ultrasound training, including competency-based medical education (CBME), simulation technologies, technology-based resources, artificial intelligence (AI), and online-learning platforms. Traditional training methods such as theoretical learning, practical experience, and peer learning are also discussed to provide a comprehensive view of current practises.

RESULTS

Innovations in ultrasound training include the use of high-fidelity simulators, virtual reality (VR), augmented reality (AR), and hybrid-learning platforms. Simulation technologies offer reproducibility, risk-free learning, diverse scenarios, and immediate feedback. AI and machine learning facilitate personalised-learning paths, real-time feedback, and automated-image analysis. Online-learning platforms and e-learning methods provide flexible, accessible, and cost-effective education. Gamification enhances learning motivation and engagement through educational games and virtual competitions.

DISCUSSION

The integration of innovative technologies in ultrasound training significantly improves diagnostic skills, learner confidence, and patient safety. However, challenges such as high costs, the need for comprehensive instructor training, and integration into existing programs must be addressed. Standardisation and certification ensure high-quality and consistent training. Future developments in AI, VR, and 3D printing promise further advancements in ultrasound education.

CONCLUSION

Innovations in ultrasound training in obstetrics offer significant improvements in medical education and patient care. The successful implementation and continuous development of these technologies are crucial to meet the growing demands of modern obstetrics.

Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality?

This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its growing prominence in healthcare. AR's integration offers a host of potential opportunities to enhance surgical precision, bolster patient engagement, and customize medical interventions. Moreover, when combined with technologies like virtual reality (VR), artificial intelligence (AI), and robotics, AR opens up new avenues for innovation in clinical practice, education, and training. However, amidst these promising prospects lie numerous unanswered questions and areas ripe for exploration. This study emphasizes the need for rigorous research to elucidate the clinical efficacy of AR-integrated interventions, optimize surgical workflows, and address technological challenges. As the healthcare landscape continues to evolve, sustained research efforts are crucial to fully realizing AR's transformative impact in medical imaging. Systematic reviews on AR in healthcare also overlook regulatory and developmental factors, particularly in regard to medical devices. These include compliance with standards, safety regulations, risk management, clinical validation, and developmental processes. Addressing these aspects will provide a comprehensive understanding of the challenges and opportunities in integrating AR into clinical settings, informing stakeholders about crucial regulatory and developmental considerations for successful implementation. Moreover, navigating the regulatory approval process requires substantial financial resources and expertise, presenting barriers to entry for smaller innovators. Collaboration across disciplines and concerted efforts to overcome barriers will be essential in navigating this frontier and harnessing the potential of AR to revolutionize healthcare delivery.

Development and validation of a hybrid simulator for ultrasound-guided laparoscopic common bile duct exploration

BACKGROUND

Ultrasound-guided laparoscopic common bile duct exploration (LCBDE) is the surgical management of choledocholithiasis. The procedure presents significant benefits to patients but still fails to be generalised because of the complex set of skills it requires. A simulator for ultrasound-guided LCBDE would allow trainee surgeons as well as experienced surgeons who perform this surgery seldomly to practice and gain confidence.

METHODS

This article presents the development and validation of an easily reproducible hybrid simulator for ultrasound-guided LCBDE which integrates real and virtual components of the task. We first developed a physical model made of silicone. The fabrication technique is replicable and allows quick and easy production of multiple models. We then applied virtual components onto the model to create training for laparoscopic ultrasound examination. Combined with a commercially available lap-trainer and surgical equipment, the model can be used for training the fundamental steps of the surgery through the trans-cystic or trans-choledochal approaches. The simulator was evaluated through face, content, and construct validation.

RESULTS

Two novices, eight middle grades, and three experts were recruited to test the simulator. The results of the face validation showed that the surgeons found the model realistic visually and felt realistic when performing the different steps of the surgery. The content validation indicated the usefulness of having a training system to practice the choledochotomy, the choledochoscopy and stone retrieval, and the suturing. The construct validation highlighted the ability of the simulator to differentiate between surgeons with various levels of expertise.

CONCLUSIONS

The hybrid simulator presented is a low-cost yet realistic model which allows the surgeons to practice the technical skills required for trans-cystic and trans-choledochal ultrasound-guided LCBDE.

SSA Net: Small Scale-Aware Enhancement Network for Human Pose Estimation

In the field of human pose estimation, heatmap-based methods have emerged as the dominant approach, and numerous studies have achieved remarkable performance based on this technique. However, the inherent drawbacks of heatmaps lead to serious performance degradation in methods based on heatmaps for smaller-scale persons. While some researchers have attempted to tackle this issue by improving the performance of small-scale persons, their efforts have been hampered by the continued reliance on heatmap-based methods. To address this issue, this paper proposes the SSA Net, which aims to enhance the detection accuracy of small-scale persons as much as possible while maintaining a balanced perception of persons at other scales. SSA Net utilizes HRNetW48 as a feature extractor and leverages the TDAA module to enhance small-scale perception. Furthermore, it abandons heatmap-based methods and instead adopts coordinate vector regression to represent keypoints. Notably, SSA Net achieved an AP of 77.4% on the COCO Validation dataset, which is superior to other heatmap-based methods. Additionally, it achieved highly competitive results on the Tiny Validation and MPII datasets as well.

CAL-Tutor: A HoloLens 2 Application for Training in Obstetric Sonography and User Motion Data Recording

Obstetric ultrasound (US) training teaches the relationship between foetal anatomy and the viewed US slice to enable navigation to standardised anatomical planes (head, abdomen and femur) where diagnostic measurements are taken. This process is difficult to learn, and results in considerable inter-operator variability. We propose the CAL-Tutor system for US training based on a US scanner and phantom, where a model of both the baby and the US slice are displayed to the trainee in its physical location using the HoloLens 2. The intention is that AR guidance will shorten the learning curve for US trainees and improve spatial awareness. In addition to the AR guidance, we also record many data streams to assess user motion and the learning process. The HoloLens 2 provides eye gaze, head and hand position, ARToolkit and NDI Aurora tracking gives the US probe positions and an external camera records the overall scene. These data can provide a rich source for further analysis, such as distinguishing expert from novice motion. We have demonstrated the system in a sample of engineers. Feedback suggests that the system helps novice users navigate the US probe to the standard plane. The data capture is successful and initial data visualisations show that meaningful information about user behaviour can be captured. Initial feedback is encouraging and shows improved user assessment where AR guidance is provided.