Welcome to this Special Issue of Wiley’s Healthcare Technology Letters (HTL) journal dedicated to the 2023 edition of the Augmented Environments for Computer-Assisted Interventions (AE-CAI), Computer Assisted and Robotic Endoscopy (CARE), and Context-aware Operating Theatres (OR 2.0) joint workshop. We are pleased to present the proceedings of this exciting scientific gathering held in conjunction with the Medical Image Computing and Computer-Assisted Interventions (MICCAI) conference on October 8th, 2023 in Vancouver, British Columbia, Canada. We hope that you will enjoy reading this Special Issue and we look forward to your continuing support and participation in future editions of the AE-CAI, CARE and OR 2.0 workshops. Their continued success demands our ongoing commitment and support, and we hope to welcome you all to the next edition of the workshop at MICCAI 2024 in Marrakesh, Marocco.

The use of head-mounted augmented reality (AR) for surgeries has grown rapidly in recent years. AR aids in intraoperative surgical navigation through overlaying 3D holographic reconstructions of medical data. However, performing AR surgeries on complex areas poses challenges in terms of accuracy and speed. This study explores the feasibility of an AR guidance system for resections of positive tumor margins at the head and neck region. We present an intraoperative solution that enables surgeons to access holographic reconstructions of resected cadaver tissues. The solution involves using 3D scanner to capture detailed scans of the resected tissue, which are uploaded into our software. It then converts these scans into holograms that are viewable through a head-mounted AR display. Surgeons navigates the tumor site by re-aligning these holograms with cadavers using gestures or voice commands. This workflow runs concurrently with frozen section analysis. On average, we achieve an uploading time of 2.98 min, visualization time of 1.05 min and re-alignment time of 4.39 min, within the 20 - 30 min window for frozen section analysis. We achieve a mean re-alignment error of 3.1 mm. Our software provides a foundation for new product development in using AR to navigate complex anatomy in surgery.

Kidney stones require surgical removal when they grow too large to be broken up externally or to pass on their own. Upper tract urothelial carcinoma are also sometimes treated endoscopically in a similar procedure. These surgeries are difficult, particularly for trainees who often miss tumors, stones or stone fragments, requiring re-operation. One cause of difficulty is the high cognitive strain surgeons experience in creating accurate mental models during the endoscopic operation. Furthermore, there are no patient-specific simulators to facilitate training or standardized visualization tools for ureteroscopy despite its high prevalence. We propose ASSIST-U, a system to automatically create realistic ureteroscopy images and videos solely using preoperative CT images to address these unmet needs. We train a 3D UNet model to automatically segment CT images and construct 3D surfaces. These surfaces are then skeletonized for rendering and camera position tracking. Finally, we train a style transfer model using Contrastive Unpaired Translation (CUT) to synthesize realistic ureteroscopy images. Cross validation on the UNet model achieved a Dice score of 0.853 $\pm$ 0.084 for the CT segmentation step. CUT style transfer produced visually plausible images; the Kernel Inception Distance to real ureteroscopy images was reduced from 0.198 (rendered) to 0.089 (synthesized). We also qualitatively demonstrate the entire pipeline from CT to synthesized ureteroscopy. The proposed ASSIST-U system shows promise for aiding surgeons in visualization of kidney ureteroscopy.

The integration of Augmented Reality (AR) into daily surgical practice is withheld by the correct registration of pre-operative data. This includes intelligent 3D model superposition whilst simultaneously handling real and virtual occlusions caused by the AR overlay. Occlusions can negatively impact surgical safety and as such deteriorate rather than improve surgical care. Robotic surgery is particularly suited to tackle these integration challenges in a stepwise approach as the robotic console allows for different inputs to be displayed in parallel to the surgeon. Nevertheless, real-time de-occlusion requires extensive computational resources which further complicates clinical integration. This work tackles the problem of instrument occlusion and presents, to our best knowledge, the first-in-human on edge deployment of a real-time binary segmentation pipeline during three robot-assisted surgeries: partial nephrectomy, migrated endovascular stent removal and liver metastasectomy. To this end, a state-of-the-art real-time segmentation and 3D model pipeline was implemented and presented to the surgeon during live surgery. The pipeline allows real-time binary segmentation of 37 non-organic surgical items, which are never occluded during AR. The application features real-time manual 3D model manipulation for correct soft tissue alignment. The proposed pipeline can contribute towards surgical safety, ergonomics and acceptance of AR in minimally invasive surgery.

Efficient communication and collaboration are essential in the operating room for successful and safe surgery. While many technologies are improving various aspects of surgery, communication between attending surgeons, residents, and surgical teams is still limited to verbal interactions that are prone to misunderstandings. Novel modes of communication can increase speed and accuracy, and transform operating rooms. We present a mixed reality (MR) based gaze sharing application on Microsoft HoloLens 2 headset that can help expert surgeons indicate specific regions, communicate with decreased verbal effort, and guide novices throughout an operation. We test the utility of the application with a user study of endoscopic kidney stone localization completed by urology expert and novice surgeons. We observe improvement in the NASA Task Load Index surveys (up to 25.23%), in the success rate of the task (6.98% increase in localized stone percentage), and in gaze analyses (up to 31.99%). The proposed application shows promise in both operating room applications and surgical training tasks.

Endoscopic renal surgeries have high re-operation rates, particularly for lower volume surgeons. Due to the limited field and depth of view of current endoscopes, mentally mapping preoperative computed tomography (CT) images of patient anatomy to the surgical field is challenging. The inability to completely navigate the intrarenal collecting system leads to missed kidney stones and tumors, subsequently raising recurrence rates. We propose a guidance system to estimate the endoscope positions within the CT to reduce re-operation rates. We use a Structure from Motion algorithm to reconstruct the kidney collecting system from the endoscope videos. In addition, we segment the kidney collecting system from CT scans using 3D U-Net to create a 3D model. We can then register the two collecting system representations to provide information on the relative endoscope position. We demonstrate correct reconstruction and localization of intrarenal anatomy and endoscope position. Furthermore, we create a 3D map supported by the RGB endoscope images to reduce the burden of mental mapping during surgery. The proposed reconstruction pipeline has been validated for guidance. It can reduce the mental burden for surgeons and is a step towards our long-term goal of reducing re-operation rates in kidney stone surgery.

This work present a proof-of-concept of a robotic-driven intra-operative scanner designed for knee cartilage lesion repair, part of a system for direct in vivo bioprinting. The proposed system is based on a photogrammetric pipeline, which reconstructs the cartilage and lesion surfaces from sets of photographs acquired by a robotic-handled endoscope, and produces 3D grafts for further printing path planning. A validation on a synthetic phantom is presented, showing that —despite the cartilage smooth and featureless surface— the current prototype can accurately reconstruct osteochondral lesions and their surroundings with mean error values of 0.199 ± 0.096 mm but with noticeable concentration on areas with poor lighting or low photographic coverage. The system can also accurately generate grafts for bioprinting, although with a slight tendency to underestimate the actual lesion sizes, producing grafts with coverage errors of -12.2 ± 3.7, -7.9 ± 4.9 and -15.2 ± 3.4 % for the medio-lateral, antero-posterior and craneo-caudal directions respectively. Improvements in lighting and acquisition for enhancing reconstruction accuracy are planned as future work, as well as integration into a complete bioprinting pipeline and validation with ex vivo phantoms.

The transparency of machine learning models is central to good practice when they are applied in high stakes applications. Recent developments make this feasible for tabular data, which is prevalent in risk modelling and computer-based decision support across multiple domains including healthcare. Important motivating factors for interpretability are outlined and practical approaches are summarised, signposting the main methods available, with pointers to the supporting literature. A key finding is that any black box classifier making probabilistic predictions of class membership from data in tabular form can be represented with a globally interpretable model without loss of performance.

ECG is a non-invasive way of determining cardiac health by measuring the electrical activity of the heart. We investigate a novel detection technique for feature points P, QRS and T to diagnose various atrial and ventricular cardiovascular anomalies with ECG signals for ambulatory monitoring. Before the system is worthy of field trials, we validated it with several databases and recorded their response. The QRS complex detection is based on the Pan Tompkins Algorithm and difference operation method that provides positive predictivity, sensitivity and false detection rate of 99.29\%, 99.49\% and 1.29 \% respectively. Proposed novel T wave detection provides sensitivity of 97.78\%. Also, proposed P wave detection provides positive predictivity, sensitivity and false detection rate of 99.43\%, 99.4\% and 1.15\% for the control study (normal subjects) and 82.68\%, 94.3\% and 25.4\% for the case (patients with cardiac anomalies) study respectively. Disease detection such as, arrhythmia is based on standard R-R intervals while myocardial infarction is based on the ST-T deviations where the positive predictivity, sensitivity and accuracy are observed to be 94.6\%, 84.2\% and 85\%, respectively. It should be noted that, since the frontal leads are only used, the anterior myocardial infarction cases are detected with the injury pattern in lead \textit{avl} and ST depression in reciprocal leads. Detection of atrial fibrillation is done for both short and long duration signals using statistical methods using interquartile range and standard deviations, giving very high accuracy, 100\% in most cases. The system hardware for obtaining the 2 lead ECG signal is designed using commercially available off the shelf components. Small field validation of the designed system is performed at a Public Health Centre in Gujarat, India with 42 patients (both cases and controls). We achieved 78.5\% accuracy during the field validation.

Today, the elderly population is increasing and they endure many drawbacks, especially defects in their knee joints which lead to improper gait. To solve this problem, their knee joint can be replaced with knee arthroplasty. In this letter, the level of improvement in the human gait before and after total knee arthroplasty (TKA) surgery is investigated using the Dynamic Time Wrapping (DTW) algorithm. For this purpose, several volunteers who have problems with their knees incorporate in a test before and after TKA surgery, and data of gait analysis is collected. Then, the obtained data is compared with a reference using the DTW algorithm. The outcome results illustrate an improvement of 89\% - 97\% by the proposed algorithm after TKA surgery. Therefore, patients can see improvement with high speed and accuracy that result in more promising for using TKA surgery.

Drug treatment in elderly patients is a challenge for healthcare systems. This challenge is exacerbated by age-related changes in pharmacokinetics and pharmacodynamics, multimorbidity, frailty and cognitive impairment. Moreover, old patients are commonly exposed to polypharmacy, leading to increased risk of drug interactions, adverse drug reactions, and higher costs for the healthcare systems. Thus, the complex task of prescribing medications to older polymedicated patients encourages the use of Clinical Decision Support Systems (CDSS). This paper evaluates the CDSS miniQ for identifying potentially inappropriate prescribing in older adults with polypharmacy and to assesses the usability and acceptability of miniQ in health care professionals, patients, and caregivers. It describes how useful is CDSS miniQ system for Primary Care physicians, as well as for patients and their caregivers to know their medications. The discussion emphasizes how this system is useful to improve the prescribing process and reduce errors.

Abstract: Background: Telemedicine can be used to meet several objectives: reduce the time needed to obtain a diagnosis, monitor patients at home, reduce the risk of intra-hospital infection, inform citizens and train health professionals. In Morocco, this technology is currently being tested. Objective: Health professionals are the main users of telemedicine systems, and their acceptance will contribute to the successful implementation of this technology. The objective of this study is to provide a better understanding of the issues surrounding the acceptance of telemedicine technology by Moroccan health professionals in the public sector, for a possible generalization of this technology in Morocco. Method: Following a literature review, we mobilized a modified version of the unified model of technology acceptance and use, to explain and understand the determinants of health professionals’ intention to accept telemedicine technology. In order to confirm the proposed model, a hybrid exploratory qualitative approach via the method of semi-directive interviews was used, with a sample of doctors and nurses who constitute, in our opinion, the main actors towards an acceptance of this technology within Moroccan hospitals

Introduction Axial spondylarthritis (axSpA) is a chronic inflammatory disease and commonly results in pain and joint stiffness. Using remote technology, such as a computer vision-aided system, has the potential to monitor functional movement and posture. Methods The validity of the remote technology measurement of functional movement and posture were tested cross-sectionally and compared to a standard clinical measurement by a physiotherapist. The feasibility of remote implementation was tested in a home environment. In addition, a cost-benefit analysis was conducted. Results Thirty-one participants with axSpA (42% female, 54(SD 13) years old and 27.4(SD 5.3) kg/m2) and 31 participants without back pain (65% female, 36(SD 10) years old and 25.9(3.7) kg/m2). In the axSpA group, the validity of assessment on cervical rotation, lumbar flexion, lumbar side flexion, shoulder flexion, hip abduction, tragus-to-wall and thoracic kyphosis showed significant moderate to strong correlation; in the non-back pain group, the same measures showed significant correlation ranging from weak to strong. Conclusions Remote technology systems in rehabilitation have the potential to reduce health inequality and improve cost and time effectiveness for both patients and the health system. Additionally, results show that using this Computer Vision-aided system in a home environment is a safe method.

This study applied the World Health Organization (WHO) guidelines to redesign the Portable Health Clinic (PHC), as a Remote Healthcare System (RHS), for the spread of COVID-19 containment. Additionally, the proposed system not only collects people data but also classifies the case according to the the main symptoms of coronavirus using the COVID-19 triage process (CT-process) based on the analysis of measurement readings taken from patients, where drones are used as a PHC platform and are equipped with the required sensors and essential COVID-19 medications for testing and treating people at their doorstep autonomously when a full curfew is imposed.