A novel affordable user interface for robotic surgery training: design, development and usability study.

Introduction: The use of robotic systems in the surgical domain has become groundbreaking for patients and surgeons in the last decades. While the annual number of robotic surgical procedures continues to increase rapidly, it is essential to provide the surgeon with innovative training courses along with the standard specialization path. To this end, simulators play a fundamental role. Currently, the high cost of the leading VR simulators limits their accessibility to educational institutions. The challenge lies in balancing high-fidelity simulation with cost-effectiveness; however, few cost-effective options exist for robotic surgery training. Methods: This paper proposes the design, development and user-centered usability study of an affordable user interface to control a surgical robot simulator. It consists of a cart equipped with two haptic interfaces, a VR visor and two pedals. The simulations were created using Unity, which offers versatility for expanding the simulator to more complex scenes. An intuitive teleoperation control of the simulated robotic instruments is achieved through a high-level control strategy. Results and Discussion: Its affordability and resemblance to real surgeon consoles make it ideal for implementing robotic surgery training programs in medical schools, enhancing accessibility to a broader audience. This is demonstrated by the results of an usability study involving expert surgeons who use surgical robots regularly, expert surgeons without robotic surgery experience, and a control group. The results of the study, which was based on a traditional Peg-board exercise and Camera Control task, demonstrate the simulator's high usability and intuitive control across diverse user groups, including those with limited experience. This offers evidence that this affordable system is a promising solution for expanding robotic surgery training.

An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning.

PURPOSE: Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination. METHODS: Several deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model. RESULTS: ResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials. CONCLUSION: The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.

Real-Time Laryngeal Cancer Boundaries Delineation on White Light and Narrow-Band Imaging Laryngoscopy with Deep Learning.

OBJECTIVE: To investigate the potential of deep learning for automatically delineating (segmenting) laryngeal cancer superficial extent on endoscopic images and videos. METHODS: A retrospective study was conducted extracting and annotating white light (WL) and Narrow-Band Imaging (NBI) frames to train a segmentation model (SegMENT-Plus). Two external datasets were used for validation. The model's performances were compared with those of two otolaryngology residents. In addition, the model was tested on real intraoperative laryngoscopy videos. RESULTS: A total of 3933 images of laryngeal cancer from 557 patients were used. The model achieved the following median values (interquartile range): Dice Similarity Coefficient (DSC) = 0.83 (0.70-0.90), Intersection over Union (IoU) = 0.83 (0.73-0.90), Accuracy = 0.97 (0.95-0.99), Inference Speed = 25.6 (25.1-26.1) frames per second. The external testing cohorts comprised 156 and 200 images. SegMENT-Plus performed similarly on all three datasets for DSC (p = 0.05) and IoU (p = 0.07). No significant differences were noticed when separately analyzing WL and NBI test images on DSC (p = 0.06) and IoU (p = 0.78) and when analyzing the model versus the two residents on DSC (p = 0.06) and IoU (Senior vs. SegMENT-Plus, p = 0.13; Junior vs. SegMENT-Plus, p = 1.00). The model was then tested on real intraoperative laryngoscopy videos. CONCLUSION: SegMENT-Plus can accurately delineate laryngeal cancer boundaries in endoscopic images, with performances equal to those of two otolaryngology residents. The results on the two external datasets demonstrate excellent generalization capabilities. The computation speed of the model allowed its application on videolaryngoscopies simulating real-time use. Clinical trials are needed to evaluate the role of this technology in surgical practice and resection margin improvement. LEVEL OF EVIDENCE: III Laryngoscope, 134:2826-2834, 2024.

Learning-based keypoint registration for fetoscopic mosaicking.

PURPOSE: In twin-to-twin transfusion syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. In the current practice, TTTS is treated surgically by closing abnormal anastomoses using laser ablation. This surgery is minimally invasive and relies on fetoscopy. Limited field of view makes anastomosis identification a challenging task for the surgeon. METHODS: To tackle this challenge, we propose a learning-based framework for in vivo fetoscopy frame registration for field-of-view expansion. The novelties of this framework rely on a learning-based keypoint proposal network and an encoding strategy to filter (i) irrelevant keypoints based on fetoscopic semantic image segmentation and (ii) inconsistent homographies. RESULTS: We validate our framework on a dataset of six intraoperative sequences from six TTTS surgeries from six different women against the most recent state-of-the-art algorithm, which relies on the segmentation of placenta vessels. CONCLUSION: The proposed framework achieves higher performance compared to the state of the art, paving the way for robust mosaicking to provide surgeons with context awareness during TTTS surgery.

Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings.

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field.

Toward a navigation framework for fetoscopy.

PURPOSE: Fetoscopic laser photocoagulation of placental anastomoses is the most effective treatment for twin-to-twin transfusion syndrome (TTTS). A robust mosaic of placenta and its vascular network could support surgeons' exploration of the placenta by enlarging the fetoscope field-of-view. In this work, we propose a learning-based framework for field-of-view expansion from intra-operative video frames. METHODS: While current state of the art for fetoscopic mosaicking builds upon the registration of anatomical landmarks which may not always be visible, our framework relies on learning-based features and keypoints, as well as robust transformer-based image-feature matching, without requiring any anatomical priors. We further address the problem of occlusion recovery and frame relocalization, relying on the computed features and their descriptors. RESULTS: Experiments were conducted on 10 in-vivo TTTS videos from two different fetal surgery centers. The proposed framework was compared with several state-of-the-art approaches, achieving higher [Formula: see text] on 7 out of 10 videos and a success rate of [Formula: see text] in occlusion recovery. CONCLUSION: This work introduces a learning-based framework for placental mosaicking with occlusion recovery from intra-operative videos using a keypoint-based strategy and features. The proposed framework can compute the placental panorama and recover even in case of camera tracking loss where other methods fail. The results suggest that the proposed framework has large potential to pave the way to creating a surgical navigation system for TTTS by providing robust field-of-view expansion.

Electric Bioimpedance Sensing for the Detection of Head and Neck Squamous Cell Carcinoma.

The early detection of head and neck squamous cell carcinoma (HNSCC) is essential to improve patient prognosis and enable organ and function preservation treatments. The objective of this study is to assess the feasibility of using electrical bioimpedance (EBI) sensing technology to detect HNSCC tissue. A prospective study was carried out analyzing tissue from 46 patients undergoing surgery for HNSCC. The goal was the correct identification of pathologic tissue using a novel needle-based EBI sensing device and AI-based classifiers. Considering the data from the overall patient cohort, the system achieved accuracies between 0.67 and 0.93 when tested on tissues from the mucosa, skin, muscle, lymph node, and cartilage. Furthermore, when considering a patient-specific setting, the accuracy range increased to values between 0.82 and 0.95. This indicates that more reliable results may be achieved when considering a tissue-specific and patient-specific tissue assessment approach. Overall, this study shows that EBI sensing may be a reliable technology to distinguish pathologic from healthy tissue in the head and neck region. This observation supports the continuation of this research on the clinical use of EBI-based devices for early detection and margin assessment of HNSCC.

Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State-of-the-Art Review.

OBJECTIVE: The endoscopic and laryngoscopic examination is paramount for laryngeal, oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesions and cancer evaluation. Nevertheless, upper aerodigestive tract (UADT) endoscopy is intrinsically operator-dependent and lacks objective quality standards. At present, there has been an increased interest in artificial intelligence (AI) applications in this area to support physicians during the examination, thus enhancing diagnostic performances. The relative novelty of this research field poses a challenge both for the reviewers and readers as clinicians often lack a specific technical background. DATA SOURCES: Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and Google Scholar. REVIEW METHODS: A structured review of the current literature (up to September 2022) was performed. Search terms related to topics of AI, machine learning (ML), and deep learning (DL) in UADT endoscopy and laryngoscopy were identified and queried by 3 independent reviewers. Citations of selected studies were also evaluated to ensure comprehensiveness. CONCLUSIONS: Forty-one studies were included in the review. AI and computer vision techniques were used to achieve 3 fundamental tasks in this field: classification, detection, and segmentation. All papers were summarized and reviewed. IMPLICATIONS FOR PRACTICE: This article comprehensively reviews the latest developments in the application of ML and DL in UADT endoscopy and laryngoscopy, as well as their future clinical implications. The technical basis of AI is also explained, providing guidance for nonexpert readers to allow critical appraisal of the evaluation metrics and the most relevant quality requirements.

Engineering and Development of a Tissue Model for the Evaluation of Microneedle Penetration Ability, Drug Diffusion, Photothermal Activity, and Ultrasound Imaging: A Promising Surrogate to Ex Vivo and In Vivo Tissues.

Driven by regulatory authorities and the ever-growing demands from industry, various artificial tissue models have been developed. Nevertheless, there is no model to date that is capable of mimicking the biomechanical properties of the skin whilst exhibiting the hydrophilicity/hydrophobicity properties of the skin layers. As a proof-of-concept study, tissue surrogates based on gel and silicone are fabricated for the evaluation of microneedle penetration, drug diffusion, photothermal activity, and ultrasound bioimaging. The silicone layer aims to imitate the stratum corneum while the gel layer aims to mimic the water-rich viable epidermis and dermis present in in vivo tissues. The diffusion of drugs across the tissue model is assessed, and the results reveal that the proposed tissue model shows similar behavior to a cancerous kidney. In place of typical in vitro aqueous solutions, this model can also be employed for evaluating the photoactivity of photothermal agents since the tissue model shows a similar heating profile to skin of mice when irradiated with near-infrared laser. In addition, the designed tissue model exhibits promising results for biomedical applications in optical coherence tomography and ultrasound imaging. Such a tissue model paves the way to reduce the use of animals testing in research whilst obviating ethical concerns.

Towards OCT-Guided Endoscopic Laser Surgery-A Review.

Optical Coherence Tomography (OCT) is an optical imaging technology occupying a unique position in the resolution vs. imaging depth spectrum. It is already well established in the field of ophthalmology, and its application in other fields of medicine is growing. This is motivated by the fact that OCT is a real-time sensing technology with high sensitivity to precancerous lesions in epithelial tissues, which can be exploited to provide valuable information to clinicians. In the prospective case of OCT-guided endoscopic laser surgery, these real-time data will be used to assist surgeons in challenging endoscopic procedures in which high-power lasers are used to eradicate diseases. The combination of OCT and laser is expected to enhance the detection of tumors, the identification of tumor margins, and ensure total disease eradication while avoiding damage to healthy tissue and critical anatomical structures. Therefore, OCT-guided endoscopic laser surgery is an important nascent research area. This paper aims to contribute to this field with a comprehensive review of state-of-the-art technologies that may be exploited as the building blocks for achieving such a system. The paper begins with a review of the principles and technical details of endoscopic OCT, highlighting challenges and proposed solutions. Then, once the state of the art of the base imaging technology is outlined, the new OCT-guided endoscopic laser surgery frontier is reviewed. Finally, the paper concludes with a discussion on the constraints, benefits and open challenges associated with this new type of surgical technology.

Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images.

Introduction: Narrow Band Imaging (NBI) is an endoscopic visualization technique useful for upper aero-digestive tract (UADT) cancer detection and margins evaluation. However, NBI analysis is strongly operator-dependent and requires high expertise, thus limiting its wider implementation. Recently, artificial intelligence (AI) has demonstrated potential for applications in UADT videoendoscopy. Among AI methods, deep learning algorithms, and especially convolutional neural networks (CNNs), are particularly suitable for delineating cancers on videoendoscopy. This study is aimed to develop a CNN for automatic semantic segmentation of UADT cancer on endoscopic images. Materials and Methods: A dataset of white light and NBI videoframes of laryngeal squamous cell carcinoma (LSCC) was collected and manually annotated. A novel DL segmentation model (SegMENT) was designed. SegMENT relies on DeepLabV3+ CNN architecture, modified using Xception as a backbone and incorporating ensemble features from other CNNs. The performance of SegMENT was compared to state-of-the-art CNNs (UNet, ResUNet, and DeepLabv3). SegMENT was then validated on two external datasets of NBI images of oropharyngeal (OPSCC) and oral cavity SCC (OSCC) obtained from a previously published study. The impact of in-domain transfer learning through an ensemble technique was evaluated on the external datasets. Results: 219 LSCC patients were retrospectively included in the study. A total of 683 videoframes composed the LSCC dataset, while the external validation cohorts of OPSCC and OCSCC contained 116 and 102 images. On the LSCC dataset, SegMENT outperformed the other DL models, obtaining the following median values: 0.68 intersection over union (IoU), 0.81 dice similarity coefficient (DSC), 0.95 recall, 0.78 precision, 0.97 accuracy. For the OCSCC and OPSCC datasets, results were superior compared to previously published data: the median performance metrics were, respectively, improved as follows: DSC=10.3% and 11.9%, recall=15.0% and 5.1%, precision=17.0% and 14.7%, accuracy=4.1% and 10.3%. Conclusion: SegMENT achieved promising performances, showing that automatic tumor segmentation in endoscopic images is feasible even within the highly heterogeneous and complex UADT environment. SegMENT outperformed the previously published results on the external validation cohorts. The model demonstrated potential for improved detection of early tumors, more precise biopsies, and better selection of resection margins.

Efficacy of High-Resolution Preoperative 3D Reconstructions for Lesion Localization in Oncological Colorectal Surgery-First Pilot Study.

When planning an operation, surgeons usually rely on traditional 2D imaging. Moreover, colon neoplastic lesions are not always easy to locate macroscopically, even during surgery. A 3D virtual model may allow surgeons to localize lesions with more precision and to better visualize the anatomy. In this study, we primary analyzed and discussed the clinical impact of using such 3D models in colorectal surgery. This is a monocentric prospective observational pilot study that includes 14 consecutive patients who presented colorectal lesions with indication for surgical therapy. A staging computed tomography (CT)/magnetic resonance imaging (MRI) scan and a colonoscopy were performed on each patient. The information gained from them was provided to obtain a 3D rendering. The 2D images were shown to the surgeon performing the operation, while the 3D reconstructions were shown to a second surgeon. Both of them had to locate the lesion and describe which procedure they would have performed; we then compared their answers with one another and with the intraoperative and histopathological findings. The lesion localizations based on the 3D models were accurate in 100% of cases, in contrast to conventional 2D CT scans, which could not detect the lesion in two patients (in these cases, lesion localization was based on colonoscopy). The 3D model reconstruction allowed an excellent concordance correlation between the estimated and the actual location of the lesion, allowing the surgeon to correctly plan the procedure with excellent results. Larger clinical studies are certainly required.

Real-time vessel segmentation and reconstruction for virtual fixtures for an active handheld microneurosurgical instrument.

PURPOSE: Complications related to vascular damage such as intra-operative bleeding may be avoided during neurosurgical procedures such as petroclival meningioma surgery. To address this and improve the patient's safety, we designed a real-time blood vessel avoidance strategy that enables operation on deformable tissue during petroclival meningioma surgery using Micron, a handheld surgical robotic tool. METHODS: We integrated real-time intra-operative blood vessel segmentation of brain vasculature using deep learning, with a 3D reconstruction algorithm to obtain the vessel point cloud in real time. We then implemented a virtual-fixture-based strategy that prevented Micron's tooltip from entering a forbidden region around the vessel, thus avoiding damage to it. RESULTS: We achieved a median Dice similarity coefficient of 0.97, 0.86, 0.87 and 0.77 on datasets of phantom blood vessels, petrosal vein, internal carotid artery and superficial vessels, respectively. We conducted trials with deformable clay vessel phantoms, keeping the forbidden region 400 [Formula: see text]m outside and 400 [Formula: see text]m inside the vessel. Micron's tip entered the forbidden region with a median penetration of just 8.84 [Formula: see text]m and 9.63 [Formula: see text]m, compared to 148.74 [Formula: see text]m and 117.17 [Formula: see text]m without our strategy, for the former and latter trials, respectively. CONCLUSION: Real-time control of Micron was achieved at 33.3 fps. We achieved improvements in real-time segmentation of brain vasculature from intra-operative images and showed that our approach works even on non-stationary vessel phantoms. The results suggest that by enabling precise, real-time control, we are one step closer to using Micron in real neurosurgical procedures.

Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection.

OBJECTIVES: To assess a new application of artificial intelligence for real-time detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow-band imaging (NBI) videolaryngoscopies based on the You-Only-Look-Once (YOLO) deep learning convolutional neural network (CNN). STUDY DESIGN: Experimental study with retrospective data. METHODS: Recorded videos of LSCC were retrospectively collected from in-office transnasal videoendoscopies and intraoperative rigid endoscopies. LSCC videoframes were extracted for training, validation, and testing of various YOLO models. Different techniques were used to enhance the image analysis: contrast limited adaptive histogram equalization, data augmentation techniques, and test time augmentation (TTA). The best-performing model was used to assess the automatic detection of LSCC in six videolaryngoscopies. RESULTS: Two hundred and nineteen patients were retrospectively enrolled. A total of 624 LSCC videoframes were extracted. The YOLO models were trained after random distribution of images into a training set (82.6%), validation set (8.2%), and testing set (9.2%). Among the various models, the ensemble algorithm (YOLOv5s with YOLOv5m-TTA) achieved the best LSCC detection results, with performance metrics in par with the results reported by other state-of-the-art detection models: 0.66 Precision (positive predicted value), 0.62 Recall (sensitivity), and 0.63 mean Average Precision at 0.5 intersection over union. Tests on the six videolaryngoscopies demonstrated an average computation time per videoframe of 0.026 seconds. Three demonstration videos are provided. CONCLUSION: This study identified a suitable CNN model for LSCC detection in WL and NBI videolaryngoscopies. Detection performances are highly promising. The limited complexity and quick computational times for LSCC detection make this model ideal for real-time processing. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:1798-1806, 2022.

µRALP and Beyond: Micro-Technologies and Systems for Robot-Assisted Endoscopic Laser Microsurgery.

Laser microsurgery is the current gold standard surgical technique for the treatment of selected diseases in delicate organs such as the larynx. However, the operations require large surgical expertise and dexterity, and face significant limitations imposed by available technology, such as the requirement for direct line of sight to the surgical field, restricted access, and direct manual control of the surgical instruments. To change this status quo, the European project µRALP pioneered research towards a complete redesign of current laser microsurgery systems, focusing on the development of robotic micro-technologies to enable endoscopic operations. This has fostered awareness and interest in this field, which presents a unique set of needs, requirements and constraints, leading to research and technological developments beyond µRALP and its research consortium. This paper reviews the achievements and key contributions of such research, providing an overview of the current state of the art in robot-assisted endoscopic laser microsurgery. The primary target application considered is phonomicrosurgery, which is a representative use case involving highly challenging microsurgical techniques for the treatment of glottic diseases. The paper starts by presenting the motivations and rationale for endoscopic laser microsurgery, which leads to the introduction of robotics as an enabling technology for improved surgical field accessibility, visualization and management. Then, research goals, achievements, and current state of different technologies that can build-up to an effective robotic system for endoscopic laser microsurgery are presented. This includes research in micro-robotic laser steering, flexible robotic endoscopes, augmented imaging, assistive surgeon-robot interfaces, and cognitive surgical systems. Innovations in each of these areas are shown to provide sizable progress towards more precise, safer and higher quality endoscopic laser microsurgeries. Yet, major impact is really expected from the full integration of such individual contributions into a complete clinical surgical robotic system, as illustrated in the end of this paper with a description of preliminary cadaver trials conducted with the integrated µRALP system. Overall, the contribution of this paper lays in outlining the current state of the art and open challenges in the area of robot-assisted endoscopic laser microsurgery, which has important clinical applications even beyond laryngology.

Deep Learning for Automatic Segmentation of Oral and Oropharyngeal Cancer Using Narrow Band Imaging: Preliminary Experience in a Clinical Perspective.

INTRODUCTION: Fully convoluted neural networks (FCNN) applied to video-analysis are of particular interest in the field of head and neck oncology, given that endoscopic examination is a crucial step in diagnosis, staging, and follow-up of patients affected by upper aero-digestive tract cancers. The aim of this study was to test FCNN-based methods for semantic segmentation of squamous cell carcinoma (SCC) of the oral cavity (OC) and oropharynx (OP). MATERIALS AND METHODS: Two datasets were retrieved from the institutional registry of a tertiary academic hospital analyzing 34 and 45 NBI endoscopic videos of OC and OP lesions, respectively. The dataset referring to the OC was composed of 110 frames, while 116 frames composed the OP dataset. Three FCNNs (U-Net, U-Net 3, and ResNet) were investigated to segment the neoplastic images. FCNNs performance was evaluated for each tested network and compared to the gold standard, represented by the manual annotation performed by expert clinicians. RESULTS: For FCNN-based segmentation of the OC dataset, the best results in terms of Dice Similarity Coefficient (Dsc) were achieved by ResNet with 5(×2) blocks and 16 filters, with a median value of 0.6559. In FCNN-based segmentation for the OP dataset, the best results in terms of Dsc were achieved by ResNet with 4(×2) blocks and 16 filters, with a median value of 0.7603. All tested FCNNs presented very high values of variance, leading to very low values of minima for all metrics evaluated. CONCLUSIONS: FCNNs have promising potential in the analysis and segmentation of OC and OP video-endoscopic images. All tested FCNN architectures demonstrated satisfying outcomes in terms of diagnostic accuracy. The inference time of the processing networks were particularly short, ranging between 14 and 115 ms, thus showing the possibility for real-time application.

Transoral laser microsurgery: feasibility of a new exoscopic HD-3D system coupled with free beam or fiber laser.

In the last decades, new technological devices and instruments have been developed to overcome the technical limits of transoral laser microsurgery. The recent introduction of 3D endoscopy seems to be a promising tool in the field of diagnostic and operative laryngology as an alternative to the traditional microlaryngoscopy. Our work aims to present a novel transoral microsurgical setting that expands the use of exoscopic systems (in this case the VITOM® 3D-HD) as an alternative to the standard operating microscope. A customized support arm and an adaptor to firmly connect the VITOM® 3D-HD camera to the laser micromanipulator were specially designed. This setup was used as an alternative to the standard operating microscope in a cohort of 17 patients affected by suspicious early to intermediate pharyngo-laryngeal neoplasms. A historical cohort of patients treated with the traditional setting and matching the same inclusion criteria was used as a reference for the duration of surgical procedures. The surgical procedures comprised 7 cordectomies, 2 endoscopic partial supraglottic laryngectomies, 4 tongue base resections, and 4 lateral oropharyngectomies or hypopharyngectomies. In 6 cases (35%), a simultaneous neck dissection was performed. The low rate of positive deep (6%) or superficial (12%) margins reinforced the safety of this platform, and the results obtained in terms of operating time were comparable to the control group (p > 0.05), which confirms the feasibility of the system. Our surgical setting setup is a convincing alternative to traditional transoral laser microsurgery for early to intermediate pharyngo-laryngeal neoplasms. The main advantages of this system are comfortable ergonomics for the first surgeon and a potential benefit in terms of teaching if applied in university hospitals, since the entire surgical team can view the same surgical 3D-HD view of the first operator. Further work is still needed to objectively compare the traditional and new technique, and to validate our preliminary clinical findings.

The GPS for surgery: A user-centered evaluation of a navigation system for laparoscopic surgery.

BACKGROUND: Unsafe surgical care has emerged as a significant public health concern, motivated by a high percentage of major complications happening during surgery, attributed to surgeons' skills and experience, and determined to be preventable. METHODS: This article presents APSurg, an Abdominal Positioning Surgical system designed to improve awareness and safety during laparoscopic surgery. The proposed system behaves like a GPS, offering an additional dynamic virtual reality view of the surgical field. RESULTS: This work presents an evaluation study in terms of accuracy, effectiveness, and usability. Tests were conducted performing a localization task on an abdomen phantom in a simulated scenario. Results show a navigation accuracy below 5 mm. The task execution time was reduced by a 15% and the performed incision dimension was reduced by a 46%, with respect to a standard setup. A custom questionnaire showed a significant positive impact in exploiting APSurg during the surgical task execution.

SmartProbe: a bioimpedance sensing system for head and neck cancer tissue detection.

OBJECTIVES: This study presents SmartProbe, an electrical bioimpedance (EBI) sensing system based on a concentric needle electrode (CNE). The system allows the use of commercial CNEs for accurate EBI measurement, and was specially developed for in-vivo real-time cancer detection. APPROACH: Considering the uncertainties in EBI measurements due to the CNE manufacturing tolerances, we propose a calibration method based on statistical learning. This is done by extracting the correlation between the measured impedance value |Z|, and the material conductivity σ, for a group of reference materials. By utilizing this correlation, the relationship of σ and |Z| can be described as a function and reconstructed using a single measurement on a reference material of known conductivity. MAIN RESULTS: This method simplifies the calibration process, and is verified experimentally. Its effectiveness is demonstrate by results that show less than 6% relative error. An additional experiment is conducted for evaluating the system's capability to detect cancerous tissue. Four types of ex-vivo human tissue from the head and neck region, including mucosa, muscle, cartilage and salivary gland, are characterized using SmartProbe. The measurements include both cancer and surrounding healthy tissue excised from 10 different patients operated on for head and neck cancer. The measured data is then processed using dimension reduction and analyzed for tissue classification. The final results show significant differences between pathologic and healthy tissues in muscle, mucosa and cartilage specimens. SIGNIFICANCE: These results are highly promising and indicate a great potential for SmartProbe to be used in various cancer detection tasks.