A new 3D-printed temporal bone: 'the SAPIENS'-specific anatomical printed-3D-model in education and new surgical simulations.

PURPOSE: Otology and neuro-otology surgeries pose significant challenges due to the intricate and variable anatomy of the temporal bone (TB), requiring extensive training. In the last years 3D-printed temporal bone models for otological dissection are becoming increasingly popular. In this study, we presented a new 3D-printed temporal bone model named 'SAPIENS', tailored for educational and surgical simulation purposes. METHODS: The 'SAPIENS' model was a collaborative effort involving a multidisciplinary team, including radiologists, software engineers, ENT specialists, and 3D-printing experts. The development process spanned from June 2022 to October 2023 at the Department of Sense Organs, Sapienza University of Rome. Acquisition of human temporal bone images; temporal bone rendering; 3D-printing; post-printing phase; 3D-printed temporal bone model dissection and validation. RESULTS: The 'SAPIENS' 3D-printed temporal bone model demonstrated a high level of anatomical accuracy, resembling the human temporal bone in both middle and inner ear anatomy. The questionnaire-based assessment by five experienced ENT surgeons yielded an average total score of 49.4 ± 1.8 out of 61, indicating a model highly similar to the human TB for both anatomy and dissection. Specific areas of excellence included external contour, sigmoid sinus contour, cortical mastoidectomy simulation, and its utility as a surgical practice simulator. CONCLUSION: We have designed and developed a 3D model of the temporal bone that closely resembles the human temporal bone. This model enables the surgical dissection of the middle ear and mastoid with an excellent degree of similarity to the dissection performed on cadaveric temporal bones.

Lung Ultrasound Signs: The Beginning. Part 3-An Accademia di Ecografia Toracica Comprehensive Review on Ultrasonographic Signs and Real Needs.

Over the last 20 years, scientific literature and interest on chest/lung ultrasound (LUS) have exponentially increased. Interpreting mixed-anatomical and artifactual-pictures determined the need of a proposal of a new nomenclature of artifacts and signs to simplify learning, spread, and implementation of this technique. The aim of this review is to collect and analyze different signs and artifacts reported in the history of chest ultrasound regarding normal lung, pleural pathologies, and lung consolidations. By reviewing the possible physical and anatomical interpretation of these artifacts and signs reported in the literature, this work aims to present the AdET (Accademia di Ecografia Toracica) proposal of nomenclature and to bring order between published studies.

Clinical Application of Pediatric Sleep Endoscopy: An International Survey.

OBJECTIVES: To investigate through an international survey the actual clinical application of drug-induced sleep endoscopy (DISE) in pediatric patients with obstructive sleep apnea (OSA) and to clarify the use, application, clinical indications, and protocol of pediatric DISE. METHODS: A specific survey about pediatric DISE was initially developed by five international otolaryngologists with expertise in pediatric sleep apnea and drug-induced sleep endoscopy and was later spread to experts in the field of sleep apnea, members of different OSA-related associations. RESULTS: A total of 101 participants who answered all the survey questions were considered in the study. Sixty-four sleep apnea experts, equivalent to 63.4% of interviewed experts, declared they would perform DISE in pediatric OSA patients. A total of 81.9% of responders agreed to consider the DISE as the first diagnostic step in children with persistent OSA after adenotonsillectomy surgery, whereas 55.4% disagreed with performing DISE at the same time of scheduled adenotonsillectomy surgery to identify other possible sites of collapse. In the case of young patients with residual OSA and only pharyngeal collapse during DISE, 51.8% of experts agreed with performing a velopharyngeal surgery. In this case, 27.7% disagreed and 21.4% were neutral. CONCLUSION: Pediatric DISE is internationally considered to be a safe and effective procedure for identifying sites of obstruction and collapse after adenotonsillectomy in children with residual OSA. This is also useful in cases of patients with craniofacial malformations, small tonsils, laryngomalacia or Down syndrome to identify the actual site(s) of collapse. Despite this evidence, our survey highlighted that pediatric DISE is not used in different sleep centers.

Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression.

Since the outbreak of COVID-19, efforts have been made towards semi-quantitative analysis of lung ultrasound (LUS) data to assess the patient's condition. Several methods have been proposed in this regard, with a focus on frame-level analysis, which was then used to assess the condition at the video and prognostic levels. However, no extensive work has been done to analyze lung conditions directly at the video level. This study proposes a novel method for video-level scoring based on compression of LUS video data into a single image and automatic classification to assess patient's condition. The method utilizes maximum, mean, and minimum intensity projection-based compression of LUS video data over time. This enables to preserve hyper- and hypo-echoic data regions, while compressing the video down to a maximum of three images. The resulting images are then classified using a convolutional neural network (CNN). Finally, the worst predicted score given among the images is assigned to the corresponding video. The results show that this compression technique can achieve a promising agreement at the prognostic level (81.62%), while the video-level agreement remains comparable with the state-of-the-art (46.19%). Conclusively, the suggested method lays down the foundation for LUS video compression, shifting from frame-level to direct video-level analysis of LUS data.

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level.

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients.

Human-to-AI Interrater Agreement for Lung Ultrasound Scoring in COVID-19 Patients.

OBJECTIVES: Lung ultrasound (LUS) has sparked significant interest during COVID-19. LUS is based on the detection and analysis of imaging patterns. Vertical artifacts and consolidations are some of the recognized patterns in COVID-19. However, the interrater reliability (IRR) of these findings has not been yet thoroughly investigated. The goal of this study is to assess IRR in LUS COVID-19 data and determine how many LUS videos and operators are required to obtain a reliable result. METHODS: A total of 1035 LUS videos from 59 COVID-19 patients were included. Videos were randomly selected from a dataset of 1807 videos and scored by six human operators (HOs). The videos were also analyzed by artificial intelligence (AI) algorithms. Fleiss' kappa coefficient results are presented, evaluated at both the video and prognostic levels. RESULTS: Findings show a stable agreement when evaluating a minimum of 500 videos. The statistical analysis illustrates that, at a video level, a Fleiss' kappa coefficient of 0.464 (95% confidence interval [CI] = 0.455-0.473) and 0.404 (95% CI = 0.396-0.412) is obtained for pairs of HOs and for AI versus HOs, respectively. At prognostic level, a Fleiss' kappa coefficient of 0.505 (95% CI = 0.448-0.562) and 0.506 (95% CI = 0.458-0.555) is obtained for pairs of HOs and for AI versus HOs, respectively. CONCLUSIONS: To examine IRR and obtain a reliable evaluation, a minimum of 500 videos are recommended. Moreover, the employed AI algorithms achieve results that are comparable with HOs. This research further provides a methodology that can be useful to benchmark future LUS studies.

Vertical Artifacts as Lung Ultrasound Signs: Trick or Trap? Part 2- An Accademia di Ecografia Toracica Position Paper on B-Lines and Sonographic Interstitial Syndrome.

Although during the last few years the lung ultrasound (LUS) technique has progressed substantially, several artifacts, which are currently observed in clinical practice, still need a solid explanation of the physical phenomena involved in their origin. This is particularly true for vertical artifacts, conventionally known as B-lines, and for their use in clinical practice. A wider consensus and a deeper understanding of the nature of these artifactual phenomena will lead to a better classification and a shared nomenclature, and, ultimately, result in a more objective correlation between anatomo-pathological data and clinical scenarios. The objective of this review is to collect and document the different signs and artifacts described in the history of chest ultrasound, with a particular focus on vertical artifacts (B-lines) and sonographic interstitial syndrome (SIS). By reviewing the possible physical and anatomical interpretation of the signs and artifacts proposed in the literature, this work also aims to bring order to the available studies and to present the AdET (Accademia di Ecografia Toracica) viewpoint in terms of nomenclature and clinical approach to the SIS.

Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees.

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.

A mid-term follow-up with a lung ultrasonographic score correlates with the severity of COVID-19 acute phase.

Lung ultrasound (LUS) has rapidly emerged in COVID-19 diagnosis and for the follow-up during the acute phase. LUS is not yet used routinely in lung damage follow-up after COVID-19 infection. We investigated the correlation between LUS score, and clinical and laboratory parameters of severity of SARS-COV-2 damage during hospitalization and at follow-up visit. Observational retrospective study including all the patients discharged from the COVID-19 wards, who attended the post-COVID outpatient clinic of the IRCCS Policlinico San Matteo in April-June 2020. 115 patients were enrolled. Follow-up visits with LUS score measurements were at a median of 38 days (IQR 28-48) after discharge. LUS scores were associated with the length of hospitalization (p < 0.001), patients' age (p = 0.036), use of non-invasive ventilation (CPAP p < 0.001 or HFNC p = 0.018), administration of corticosteroids therapy (p = 0.030), and laboratory parameters during the acute phase (WBC p < 0.001, LDH p < 0.001, CRP p < 0.001, D-dimer p = 0.008, IL-6 p = 0.045), and inversely correlated with lymphocyte count (p = 0.007). We found correlation between LUS score and both LDH (p = 0.001) and the antibody anti-SARS-CoV-2 titers (p value = 0.008). Most of these finding were confirmed by dichothomizing the LUS score (≤ 9 or > 9 points). We found a significantly higher LUS score at the follow-up in the patients with persistent dyspnea (7.00, IQR 3.00-11.00) when compared to eupnoeic patients (3.00, IQR 0-7.00 p < 0.001). LUS score at follow-up visit correlates with more severe lung disease. These findings support the hypothesis that ultrasound could be a valid tool in the follow-up medium-term COVID-19 lung damage.

Mid-term follow-up chest CT findings in recovered COVID-19 patients with residual symptoms.

OBJECTIVES: More than a year has passed since the initial outbreak of SARS-CoV-2, which caused many hospitalizations worldwide due to COVID-19 pneumonia and its complications. However, there is still a lack of information detailing short- and long-term outcomes of previously hospitalized patients. The purpose of this study is to analyze the most frequent lung CT findings in recovered COVID-19 patients at mid-term follow-ups. METHODS: A total of 407 consecutive COVID-19 patients who were admitted to the Fondazione IRCCS Policlinico San Matteo, Pavia and discharged between February 27, 2020, and June 26, 2020 were recruited into this study. Out of these patients, a subset of 108 patients who presented with residual asthenia and dyspnea at discharge, altered spirometric data, positive lung ultrasound and positive chest X-ray was subsequently selected, and was scheduled to undergo a mid-term chest CT study, which was evaluated for specific lung alterations and morphological patterns. RESULTS: The most frequently observed lung CT alterations, in order of frequency, were ground-glass opacities (81%), linear opacities (74%), bronchiolectases (64.81%), and reticular opacities (63.88%). The most common morphological pattern was the non-specific interstitial pneumonia pattern (63.88%). Features consistent with pulmonary fibrosis were observed in 32 patients (29.62%). CONCLUSIONS: Our work showed that recovered COVID-19 patients who were hospitalized and who exhibited residual symptoms after discharge had a slow radiological recovery with persistent residual lung alterations. ADVANCES IN KNOWLEDGE: This slow recovery process should be kept in mind when determining the follow-up phases in order to improve the long-term management of patients affected by COVID-19.

New International Guidelines and Consensus on the Use of Lung Ultrasound.

Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements.

State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses.

Lung ultrasound (LUS) has been increasingly expanding since the 1990s, when the clinical relevance of vertical artifacts was first reported. However, the massive spread of LUS is only recent and is associated with the coronavirus disease 2019 (COVID-19) pandemic, during which semi-quantitative computer-aided techniques were proposed to automatically classify LUS data. In this review, we discuss the state of the art in LUS, from semi-quantitative image analysis approaches to quantitative techniques involving the analysis of radiofrequency data. We also discuss recent in vitro and in silico studies, as well as research on LUS safety. Finally, conclusions are drawn highlighting the potential future of LUS.

Quality of Life and Excessive Daytime Sleepiness in Adults with Obstructive Sleep Apnea Who Are Treated with Multilevel Surgery or Adherent to Continuous Positive Airway Pressure.

Obstructive Sleep Apnea (OSA) syndrome is a respiratory sleep disorder characterized by a reduction (hypopnea) in or a complete cessation (apnea) of airflow in the upper airways at night, in the presence of breathing effort. The gold standard treatment for OSA is ventilation through continuous positive airway pressure (CPAP), although this often shows poor patient compliance. In recent years, transoral robotic surgery (TORS) has been proposed as a valid surgical treatment for patients suffering from OSA in a multilevel surgical setting. The aim of this study is to analyze the effects on QoL and daytime sleepiness of multilevel surgery for OSA (barbed pharyngoplasty + transoral robotic surgery). Furthermore, we compared the impact on QoL and daytime sleepiness of two different treatments for patients with moderate to severe OSA, such as CPAP and TORS. Sixty-seven OSA patients who underwent multilevel robotic surgery and sixty-seven OSA patients treated with CPAP were enrolled, defined as Group 1 and Group 2, respectively. The Glasgow Benefit Inventory (GBI) questionnaire was administrated to evaluate the changes in the QoL. Respiratory outcomes were evaluated and compared. Group 1 showed a GBI total average value of +30.4, whereas Group 2, a value of +33.2 (p = 0.4). General benefit score showed no difference between groups (p = 0.1). Better values of social status benefit (p = 0.0006) emerged in the CPAP Group, whereas greater physical status benefit (p = 0.04) was shown in the TORS Group. Delta-AHI (-23.7 ± 14.3 vs. -31.7 ± 15.6; p = 0.001) and Delta-ODI (-24.5 ± 9.5 vs. -29.4 ± 10.5; p = 0.001) showed better values in the CPAP group. Therapeutic success rate of the Multilevel TORS Group was 73.1% and 91% in the CPAP group (p = 0.01), respectively. Multilevel TORS and CPAP have a positive effect on the quality of life of OSA patients. Greater social support has been reported in the CPAP group and better physical health status in the TORS group. No statistical difference emerged in the reduction in daytime sleepiness between both groups.

Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients.

The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research community. With the ongoing COVID-19 pandemic, many efforts have been made to evaluate LUS data. A four-level scoring system has been introduced to semiquantitatively assess the state of the lung, classifying the patients. Various deep learning (DL) algorithms supported with clinical validations have been proposed to automate the stratification process. However, no work has been done to evaluate the impact on the automated decision by varying pixel resolution and bit depth, leading to the reduction in size of overall data. This article evaluates the performance of DL algorithm over LUS data with varying pixel and gray-level resolution. The algorithm is evaluated over a dataset of 448 LUS videos captured from 34 examinations of 20 patients. All videos are resampled by a factor of 2, 3, and 4 of original resolution, and quantized to 128, 64, and 32 levels, followed by score prediction. The results indicate that the automated scoring shows negligible variation in accuracy when it comes to the quantization of intensity levels only. Combined effect of intensity quantization with spatial down-sampling resulted in a prognostic agreement ranging from 73.5% to 82.3%.These results also suggest that such level of prognostic agreement can be achieved over evaluation of data reduced to 32 times of its original size. Thus, laying foundation to efficient processing of data in resource constrained environments.

ECHOPAEDIA: Echography in Paediatric Patients in the Age of Coronavirus Disease 2019: Utility of Lung Ultrasound and Chest X-Ray in Diagnosis of Community-Acquired Pneumonia and Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia.

Background: In recent years, lung ultrasound (LUS) has spread to emergency departments and clinical practise gaining great support, especially in time of pandemic, but only a few studies have been done on children. The aim of the present study is to compare the diagnostic accuracy of LUS (using Soldati LUS score) and that of chest X-ray (CXR) in CAP and COVID-19 pneumonia in paediatric patients. Secondary objective of the study is to examine the association between LUS score and disease severity. Finally, we describe the local epidemiology of paediatric CAP during the study period in the era of COVID-19 by comparing it with the previous 2 years. Methods: This is an observational retrospective single-centre study carried out on patients aged 18 or younger and over the month of age admitted to the Paediatric Unit of our Foundation for suspected community-acquired pneumonia or SARS-CoV-2 pneumonia during the third pandemic wave of COVID-19. Quantitative variables were elaborated with Shapiro-Wilks test or median and interquartile range (IQR). Student's t-test was used for independent data. Association between quantitative data was evaluated with Pearson correlation. ROC curve analysis was used to calculate best cut-off of LUS score in paediatric patients. Area under the ROC curve (AUC), sensibility, and specificity are also reported with 95% confidence interval (CI). Results: The diagnostic accuracy of the LUS score in pneumonia, the area underlying the ROC curve (AUC) was 0.67 (95% CI: 0.27-1) thus showing a discrete discriminatory power, with a sensitivity of 89.66% and specificity 50% setting a LUS score greater than or equal to 1 as the best cut-off. Nine patients required oxygen support and a significant statistical correlation (p = 0.0033) emerged between LUS score and oxygen therapy. The mean LUS score in patients requiring oxygen therapy was 12. RCP was positively correlated to the patient's LUS score (p = 0.0024). Conclusions: Our study has shown that LUS is a valid alternative to CXR. Our results show how LUS score can be applied effectively for the diagnosis and stratification of paediatric pneumonia.

Lung Ultrasound in COVID-19 and Post-COVID-19 Patients, an Evidence-Based Approach.

OBJECTIVES: Worldwide, lung ultrasound (LUS) was utilized to assess coronavirus disease 2019 (COVID-19) patients. Often, imaging protocols were however defined arbitrarily and not following an evidence-based approach. Moreover, extensive studies on LUS in post-COVID-19 patients are currently lacking. This study analyses the impact of different LUS imaging protocols on the evaluation of COVID-19 and post-COVID-19 LUS data. METHODS: LUS data from 220 patients were collected, 100 COVID-19 positive and 120 post-COVID-19. A validated and standardized imaging protocol based on 14 scanning areas and a 4-level scoring system was implemented. We utilized this dataset to compare the capability of 5 imaging protocols, respectively based on 4, 8, 10, 12, and 14 scanning areas, to intercept the most important LUS findings. This to evaluate the optimal trade-off between a time-efficient imaging protocol and an accurate LUS examination. We also performed a longitudinal study, aimed at investigating how to eventually simplify the protocol during follow-up. Additionally, we present results on the agreement between AI models and LUS experts with respect to LUS data evaluation. RESULTS: A 12-areas protocol emerges as the optimal trade-off, for both COVID-19 and post-COVID-19 patients. For what concerns follow-up studies, it appears not to be possible to reduce the number of scanning areas. Finally, COVID-19 and post-COVID-19 LUS data seem to show differences capable to confuse AI models that were not trained on post-COVID-19 data, supporting the hypothesis of the existence of LUS patterns specific to post-COVID-19 patients. CONCLUSIONS: A 12-areas acquisition protocol is recommended for both COVID-19 and post-COVID-19 patients, also during follow-up.

Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound With Applications to COVID-19.

Lung ultrasound (LUS) is a cheap, safe and non-invasive imaging modality that can be performed at patient bed-side. However, to date LUS is not widely adopted due to lack of trained personnel required for interpreting the acquired LUS frames. In this work we propose a framework for training deep artificial neural networks for interpreting LUS, which may promote broader use of LUS. When using LUS to evaluate a patient's condition, both anatomical phenomena (e.g., the pleural line, presence of consolidations), as well as sonographic artifacts (such as A- and B-lines) are of importance. In our framework, we integrate domain knowledge into deep neural networks by inputting anatomical features and LUS artifacts in the form of additional channels containing pleural and vertical artifacts masks along with the raw LUS frames. By explicitly supplying this domain knowledge, standard off-the-shelf neural networks can be rapidly and efficiently finetuned to accomplish various tasks on LUS data, such as frame classification or semantic segmentation. Our framework allows for a unified treatment of LUS frames captured by either convex or linear probes. We evaluated our proposed framework on the task of COVID-19 severity assessment using the ICLUS dataset. In particular, we finetuned simple image classification models to predict per-frame COVID-19 severity score. We also trained a semantic segmentation model to predict per-pixel COVID-19 severity annotations. Using the combined raw LUS frames and the detected lines for both tasks, our off-the-shelf models performed better than complicated models specifically designed for these tasks, exemplifying the efficacy of our framework.