Design and evaluation of an anthropomorphic neck phantom for improved ultrasound diagnostics of thyroid gland tumors.

PURPOSE: Thyroid cancer is one of the most common cancers worldwide, with ultrasound-guided biopsy being the method of choice for its early detection. The accuracy of diagnostics directly depends on the qualifications of the ultrasonographers, whose performance can be enhanced through training with phantoms. The aim of this study is to propose a reproducible methodology for designing a neck phantom for ultrasound training and research from widely available materials and to validate its applicability. METHODS: The phantom was made using polyvinyl chloride mixed with additives to reproduce different levels of brightness on ultrasound screens. 3D printing and casting were used to create the neck model and various structures of the neck, including bones, cartilage, arteries, veins, lymph nodes, thyroid gland, and soft tissues. The small objects, such as tumor and lymph node models, were shaped manually. All the phantom's materials were carefully selected to match the ultrasonic speed and attenuation values of real soft tissues and bones. RESULTS: The thyroid gland contains models of a cancerous and cystic nodule. In the neck, there are models of carotid arteries and jugular veins filled with ultrasound-transparent gel. Additionally, there are replicas of lymph nodes and bone structures such as hyoid bone, thyroid cartilage, trachea, and vertebrae. The resulting phantom covers the entire neck area and has been positively received by practicing ultrasound specialists. CONCLUSIONS: The proposed manufacturing technology offers a reliable and cost-effective approach to produce an anthropomorphic neck phantom for ultrasound diagnosis of the thyroid gland. The realistic simulation provided by the phantom enhances the quality and accuracy of ultrasound examinations, contributing to better training for medical professionals and improved patient care. Subsequent research efforts can concentrate on refining the fabrication process and exploring additional features to enhance the phantom's capabilities.

Clinical application of radiological AI for pulmonary nodule evaluation: Replicability and susceptibility to the population shift caused by the COVID-19 pandemic.

PURPOSE: replicability and generalizability of medical AI are the recognized challenges that hinder a broad AI deployment in clinical practice. Pulmonary nodes detection and characterization based on chest CT images is one of the demanded use cases for automatization by means of AI, and multiple AI solutions addressing this task are becoming available. Here, we evaluated and compared the performance of several commercially available radiological AI with the same clinical task on the same external datasets acquired before and during the pandemic of COVID-19. APPROACH: 5 commercially available AI models for pulmonary nodule detection were tested on two external datasets labelled by experts according to the intended clinical task. Dataset1 was acquired before the pandemic and did not contain radiological signs of COVID-19; dataset2 was collected during the pandemic and did contain radiological signs of COVID-19. ROC-analysis was applied separately for the dataset1 and dataset2 to select probability thresholds for each dataset separately. AUROC, sensitivity and specificity metrics were used to assess and compare the results of AI performance. RESULTS: Statistically significant differences in AUROC values were observed between the AI models for the dataset1. Whereas for the dataset2 the differences of AUROC values became statistically insignificant. Sensitivity and specificity differed statistically significantly between the AI models for the dataset1. This difference was insignificant for the dataset2 when we applied the probability threshold initially selected for the dataset1. An update of the probability threshold based on the dataset2 created statistically significant differences of sensitivity and specificity between AI models for the dataset2. For 3 out of 5 AI models, the update of the probability threshold was valuable to compensate for the degradation of AI model performances with the population shift caused by the pandemic. CONCLUSIONS: Population shift in the data is able to deteriorate differences of AI models performance. Update of the probability threshold together with the population shift seems to be valuable to preserve AI models performance without retraining them.

An International Non-Inferiority Study for the Benchmarking of AI for Routine Radiology Cases: Chest X-ray, Fluorography and Mammography.

An international reader study was conducted to gauge an average diagnostic accuracy of radiologists interpreting chest X-ray images, including those from fluorography and mammography, and establish requirements for stand-alone radiological artificial intelligence (AI) models. The retrospective studies in the datasets were labelled as containing or not containing target pathological findings based on a consensus of two experienced radiologists, and the results of a laboratory test and follow-up examination, where applicable. A total of 204 radiologists from 11 countries with various experience performed an assessment of the dataset with a 5-point Likert scale via a web platform. Eight commercial radiological AI models analyzed the same dataset. The AI AUROC was 0.87 (95% CI:0.83-0.9) versus 0.96 (95% CI 0.94-0.97) for radiologists. The sensitivity and specificity of AI versus radiologists were 0.71 (95% CI 0.64-0.78) versus 0.91 (95% CI 0.86-0.95) and 0.93 (95% CI 0.89-0.96) versus 0.9 (95% CI 0.85-0.94) for AI. The overall diagnostic accuracy of radiologists was superior to AI for chest X-ray and mammography. However, the accuracy of AI was noninferior to the least experienced radiologists for mammography and fluorography, and to all radiologists for chest X-ray. Therefore, an AI-based first reading could be recommended to reduce the workload burden of radiologists for the most common radiological studies such as chest X-ray and mammography.

Maxillo-mandibular Defect Reconstruction with Bilateral Free Fibula Flaps with Dental Implant Placement and Immediate Loading: A Case Report of the Three-team Approach.

Patients with advanced malignant tumors, including both jaws, is a challenging task for a head and neck surgeon. Current treatment landscape demonstrates good functional, anatomical, and aesthetic results in patients who could previously receive only palliative care. The extensive tissue defects resulting from oncological resections in the head and neck region require immediate reconstruction due to the exposure of vital structures and their contact with the external environment. A patient was operated using a three-team multidisciplinary approach involving simultaneous work of three specialized teams of maxillofacial and reconstructive microsurgeons, as well as an implantologist and a prosthodontist. This approach allowed simultaneous tumor resection with subsequent reconstruction of the intraoperative defect involving bilateral harvesting of two revascularized free fibular osteomusculocutaneous flaps with dental implantation and simultaneous rehabilitation of dentition with crowns.

Palm Oil Fuel Ash-Based Eco-Efficient Concrete: A Critical Review of the Short-Term Properties.

The huge demand for concrete is predicted to upsurge due to rapid construction developments. Environmental worries regarding the large amounts of carbon dioxide emanations from cement production have resulted in new ideas to develop supplemental cementing materials, aiming to decrease the cement volume required for making concrete. Palm-oil-fuel-ash (POFA) is an industrial byproduct derived from palm oil waste's incineration in power plants' electricity generation. POFA has high pozzolanic characteristics. It is highly reactive and exhibits satisfactory micro-filling ability and unique properties. POFA is commonly used as a partially-alternated binder to Portland cement materials to make POFA-based eco-efficient concrete to build building using a green material. This paper presents a review of the material source, chemical composition, clean production and short-term properties of POFA. A review of related literature provides comprehensive insights into the potential application of POFA-based eco-efficient concrete in the construction industry today.