Performance of a commercially available Generative Pre-trained Transformer (GPT) in describing radiolucent lesions in panoramic radiographs and establishing differential diagnoses.

OBJECTIVES: To evaluate the performance of a commercially available Generative Pre-trained Transformer (GPT) in describing and establishing differential diagnoses for radiolucent lesions in panoramic radiographs. MATERIALS AND METHODS: Twenty-eight panoramic radiographs, each containing a single radiolucent lesion, were evaluated in consensus by three examiners and a commercially available ChatGPT-3.5 model. They provided descriptions regarding internal structure (radiodensity, loculation), periphery (margin type, cortication), shape, location (bone, side, region, teeth/structures), and effects on adjacent structures (effect, adjacent structure). Diagnostic impressions related to origin, behavior, and nature were also provided. The GPT program was additionally prompted to provide differential diagnoses. Keywords used by the GPT program were compared to those used by the examiners and scored as 0 (incorrect), 0.5 (partially correct), or 1 (correct). Mean score values and standard deviation were calculated for each description. Performance in establishing differential diagnoses was assessed using Rank-1, -2, and - 3. RESULTS: Descriptions of margination, affected bone, and origin received the highest scores: 0.93, 0.93, and 0.87, respectively. Shape, region, teeth/structures, effect, affected region, and nature received considerably lower scores ranging from 0.22 to 0.50. Rank-1, -2, and - 3 demonstrated accuracy in 25%, 57.14%, and 67.85% of cases, respectively. CONCLUSION: The performance of the GPT program in describing and providing differential diagnoses for radiolucent lesions in panoramic radiographs is variable and at this stage limited in its use for clinical application. CLINICAL RELEVANCE: Understanding the potential role of GPT systems as an auxiliary tool in image interpretation is imperative to validate their clinical applicability.

State of the art and prospects for artificial intelligence in orthognathic surgery: A systematic review with meta-analysis.

OBJECTIVE: To present a systematic review of the state of the art regarding clinical applications, main features, and outcomes of artificial intelligence (AI) in orthognathic surgery. METHODS: The PICOS strategy was performed on a systematic review (SR) to answer the following question: "What are the state of the art, characteristics and outcomes of applications with artificial intelligence for orthognathic surgery?" After registering in PROSPERO (CRD42021270789) a systematic search was performed in the databases: PubMed (including MedLine), Scopus, Embase, LILACS, MEDLINE EBSCOHOST and Cochrane Library. 195 studies were selected, after screening titles and abstracts, of which thirteen manuscripts were included in the qualitative analysis and six in the quantitative analysis. The treatment effects were plotted in a Forest-plot. JBI questionnaire for observational studies was used to asses the risk of bias. The quality of the SR evidence was assessed using the GRADE tool. RESULTS: AI studies on 2D cephalometry for orthognathic surgery, the Tau2 = 0.00, Chi2 = 3.78, p = 1.00 and I² of 0 %, indicating low heterogeneity, AI did not differ statistically from control (p = 0.79). AI studies in the diagnosis of the decision of whether or not to perform orthognathic surgery showed heterogeneity, and therefore meta-analysis was not peformed. CONCLUSION: The outcome of AI is similar to the control group, with a low degree of bias, highlighting its potential for use in various applications.

Assessment of accuracy and reproducibility of cephalometric identification performed by 2 artificial intelligence-driven tracing applications and human examiners.

OBJECTIVE: To assess the accuracy and reproducibility of cephalometric landmark identification performed by 2 artificial intelligence (AI)-driven applications (CefBot and WebCeph) and human examiners. STUDY DESIGN: Lateral cephalometric radiographs of 10 skulls containing 0.5 mm lead spheres directly placed at 10 cephalometric landmarks were obtained as the reference standard. Ten radiographs without spheres were obtained from the same skulls for identification of cephalometric points performed by the AI applications and 10 examiners. The x- and y-coordinate values of the cephalometric points identified by the AI applications and examiners were compared with those from the reference standard images using one-way analysis of variance and the Dunnet post-hoc test. The intraclass correlation coefficient (ICC) was used to evaluate reproducibility. Mean radial error (MRE) in identification was calculated with respect to the reference standard. Statistical significance was established at P < .05. RESULTS: Landmark identification by CefBot and the examiners did not exhibit significant differences from the reference standard on either axis (P > .05). WebCeph produced a significant difference (P < .05) in 4 and 6 points on the x- and y-axes, respectively. Reproducibility was excellent for CefBot and the examiners (ICC ≥ 0.9943) and good for WebCeph (ICC ≥ 0.7868). MREs of CefBot and the examiners were similar. CONCLUSION: With results similar to those of human examiners, CefBot demonstrated excellent reliability and can aid in cephalometric applications. WebCeph produced significant errors.

Metaverse and oral and maxillofacial radiology: Where do they meet?

Since previous literatureregarding the application of the metaverse in educationis scarce, the present letter aimed to highlight possible applications, as a complementary tool for the classroom, in the oral and maxillofacial radiology academic experience.Thepotential risksof the metaverse are also discussed. The metaverse and its possible applications, especially related to enhanced teaching and learning, will become a hot topic in the near future, and therefore, there will be a challenging learning curve before the educator makes the most of these innovative educational tools empowered by deeply interactive virtual reality technology.

ChatGPT: a tool for scientific writing or a threat to integrity?

The use of ChatGPT as a tool for writing and knowledge integration raises concerns about the potential for its use to replace critical thinking and academic writing skills. While ChatGPT can assist in generating text and suggesting appropriate language, it should not replace the human responsibility for creating innovative knowledge through experiential learning. The accuracy and quality of information provided by ChatGPT also require caution, as previous studies have reported inaccuracies in references used by chatbots. ChatGPT acknowledges certain limitations, including the potential for generating erroneous or biased content, and it is essential to exercise caution in interpreting its responses and recognize the indispensable role of human experience in the processes of information retrieval and knowledge creation. Furthermore, the challenge of distinguishing between papers written by humans or AI highlights the need for thorough review processes to prevent the spread of articles that could lead to the loss of confidence in the accuracy and integrity of scientific research. Overall, while the use of ChatGPT can be helpful, it is crucial to raise awareness of the potential issues associated with the use of ChatGPT, as well as to discuss boundaries so that AI can be used without compromising the quality of scientific articles and the integrity of evidence-based knowledge.

Assessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using the an artificial intelligence software.

OBJECTIVES: To evaluate the reliability and reproducibility of an artificial intelligence (AI) software in identifying cephalometric points on lateral cephalometric radiographs considering four settings of brightness and contrast. METHODS AND MATERIALS: Brightness and contrast of 30 lateral cephalometric radiographs were adjusted into four different settings. Then, the control examiner (ECont), the calibrated examiner (ECal), and the CEFBOT AI software (AIs) each marked 19 cephalometric points on all radiographs. Reliability was assessed with a second analysis of the radiographs 15 days after the first one. Statistical significance was set at p < 0.05. RESULTS: Reliability of landmark identification was excellent for the human examiners and the AIs regardless of the type of brightness and contrast setting (mean intraclass correlation coefficient >0.89). When ECont and ECal were compared for reproducibility, there were more cephalometric points with significant differences on the x-axis of the image with the highest contrast and the lowest brightness, namely N(p = 0.033), S(p = 0.030), Po(p < 0.001), and Pog'(p = 0.012). Between ECont and AIs, there were more cephalometric points with significant differences on the image with the highest contrast and the lowest brightness, namely N(p = 0.034), Or(p = 0.048), Po(p < 0.001), A(p = 0.042), Pog'(p = 0.004), Ll(p = 0.005), Ul(p < 0.001), and Sn(p = 0.001). CONCLUSIONS: While the reliability of the AIs for cephalometric landmark identification was rated as excellent, low brightness and high contrast seemed to affect its reproducibility. The experienced human examiner, on the other hand, did not show such faulty reproducibility; therefore, the AIs used in this study is an excellent auxiliary tool for cephalometric analysis, but still depends on human supervision to be clinically reliable.

Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett's analysis: can we trust a bot to do that?

OBJECTIVE: To assess the reliability of CEFBOT, an artificial intelligence (AI)-based cephalometry software, for cephalometric landmark annotation and linear and angular measurements according to Arnett's analysis. METHODS: Thirty lateral cephalometric radiographs acquired with a Carestream CS 9000 3D unit (Carestream Health Inc., Rochester/NY) were used in this study. The 66 landmarks and the 10 selected linear and angular measurements of Arnett's analysis were identified on each radiograph by a trained human examiner (control) and by CEFBOT (RadioMemory Ltd., Belo Horizonte, Brazil). For both methods, landmark annotations and measurements were duplicated with an interval of 15 days between measurements and the intraclass correlation coefficient (ICC) was calculated to determine reliability. The numerical values obtained with the two methods were compared by a t-test for independent variables. RESULTS: CEFBOT was able to perform all but one of the 10 measurements. ICC values > 0.94 were found for the remaining eight measurements, while the Frankfurt horizontal plane - true horizontal line (THL) angular measurement showed the lowest reproducibility (human, ICC = 0.876; CEFBOT, ICC = 0.768). Measurements performed by the human examiner and by CEFBOT were not statistically different. CONCLUSION: Within the limitations of our methodology, we concluded that the AI contained in the CEFBOT software can be considered a promising tool for enhancing the capacities of human radiologists.

Estado da arte da Inteligência Artifical (IA) na radiologia odontológica: revisão sistemática / The state of art of Artifical Intelligence (AI) in dental radiology: asystematic review / El estado del arte de la Inteligencia Artifical (IA) en radiología dental: una revisión sistemática

Introdução: Inteligência artificial (IA) é a capacidade de imitar a função do cérebro. É uma tecnologia que utiliza o aprendizado de máquina, rede neurais artificiais e a aprendizagem profunda. Ademais, utilizam de algoritmos aprimorados para "conhecer" recursos de um grande volume de dados da saúde para contribuir na atividade clínica, proporcionando um resultado mais rápido, preciso, reduzindo assim os erros de diagnóstico. Objetivo: O objetivo desta revisão sistemática é discorrer sobre o estado da arte na inteligência artificial na Radiologia Odontológica. Material e método: Na busca de evidências foram consultadas as bases de dados MEDLINE, PubMed, BBO,LILACS, BIREME, Google Acadêmico, e COCHRANE, por meio da estratégia PICOS. Todo o processo de avaliação e seleção foi executado por dois examinadores independentes. Resultados: Foram encontrados 878 artigos, seguindo os critérios de elegibilidade, os títulos e resumos foram analisados e 778 resumos excluídos do estudo, 10 textos completos, e finalmente 10 estudos foram incluídos no trabalho. Conclusão: Concluiu-se que os resultados obtidos ratificam que tanto o aprendizado profundo quanto o aprendizado de máquina e rede neurais artificiais são um campo precursor que mostram resultados animadores, principalmente pelo relevante auxilio prestado ao profissional inexperiente e por proporcionar um diagnóstico mais preciso e rápido. A inteligência artificial associada a radiologia odontológica evidencia a otimização do tempo, precisão diagnóstica, elaboração de tratamentos personalizados e previsão da eficácia no tratamento, características estas que contribuem para melhor qualidade no atendimento e, portanto, mais uma ferramenta de auxílio para os profissionais da radiologia odontológica(AU)

Introduction: Artificial intelligence (AI) is the ability to imitate brain function. It is a technology that uses machine learning, artificial neural networks and deep learning. In addition, they use improved algorithms to "know" resources from a large volume of health data to contribute to clinical activity, providing a faster and more accurate result, thus reducing diagnostic errors. Aim: The aim of this systematic review is to discuss the state of the art in artificial intelligence in Dental Radiology. Material and method: In the search for evidence, the MEDLINE, PubMed, BBO, LILACS, BIREME, Google Scholar, and COCHRANE databases were consulted, using the PICOS strategy. The entire evaluation and selection process was carried out by two independent examiners. Results: 878 articles were found, following the eligibility criteria, the titles and abstracts were analyzed and 778 abstracts were excluded from the study, 10 full texts, and finally 10 studies were included in the work. Conclusion: It was concluded that the results obtained confirm that both deep learning and machine and artificial neural network learning are a precursor field that show encouraging results, mainly for the relevant assistance provided to inexperienced professionals andfor providing a more accurate and quick diagnosis. The artificial intelligence associated with dental radiology shows the optimization of time, precision diagnostic, elaboration of personalized treatments and prediction of treatment effectiveness,characteristics that contribute to better quality of care and, therefore, another aid tool for radiology professional(AU)

Introducción: La inteligencia artificial (IA) es la capacidad de imitar la función cerebral. Es una tecnología que utiliza aprendizaje automático, redes neuronales artificiales y aprendizaje profundo. Además, utilizan algoritmos mejorados para "conocer" los recursos de un gran volumen de datos de salud para contribuir a la actividad clínica, proporcionando un resultado más rápido y preciso, reduciendo así los errores de diagnóstico. Objetivo: El objetivo de esta revisión sistemática es discutir el estado del arte en inteligencia artificial en radiología dental. Material y método: En la búsqueda de evidencia, se consultaron las bases de datos MEDLINE, PubMed, BBO, LILACS, BIREME, Google Scholar y COCHRANE, utilizando la estrategia PICOS. Todo el proceso de evaluación y selección fue realizado por dos examinadores independientes. Resultados: Se encontraron 878 artículos, siguiendo los criterios de elegibilidad, se analizaron los títulos y resúmenes y se excluyeron 778 resúmenes del estudio, 10 textos completos y finalmente se incluyeron 10 estudios en el trabajo. Conclusión: Se llegó a la conclusión de que los resultados obtenidos confirman que tanto el aprendizaje profundo como el aprendizaje de máquinas y redes neuronales artificiales son un campo precursor que muestra resultados alentadores, principalmente por la asistencia relevante brindada a profesionales sin experiencia y por proporcionar un diagnóstico más preciso y rápido. La inteligencia artificial asociada conla radiología dental muestra la optimización del tiempo, la precisión diagnóstica, la elaboración de tratamientos personalizados y la predicción de la efectividad del tratamiento, características que contribuyen a una mejor calidad de la atención y, por lotanto, otra herramienta de ayuda para los profesionales de la radiología odontológica(AU)

Fratura cominutiva de mandíbula por fogos de artifício: relato de caso / Comminuted mandible fracture by firework: a case report

As fraturas mandibulares são comuns na rotina dos serviços de Cirurgia e Traumatologia Buco-maxilo-facial. Embora sejam raras, as fraturas ocasionadas por fogos de artifício merecem atenção devido ao poder de destruição. O presente trabalho tem como objetivo relatar um caso de fratura mandibular cominutiva, com perda de substância dos tecidos duros e moles da face e cavidade oral. Nesse caso, foi preciso celeridade ao tratamento cirúrgico de urgência, para assegurar as vias aéreas do paciente e, em seguida, reconstruir as áreas destruídas, tendo todo o cuidado em relação ao acompanhamento do caso devido aos riscos de infecção e possíveis sequelas... (AU)

The mandible fractures are common in routine of Oral & Maxillofacial Surgery services. However, the ones occasioned by fireworks are rare. Nevertheless, they deserve attention because of the their destruction power. The present study aims to report a case of comminuted mandible fracture, with loss of substance from hard and soft tissues from the face and mouth. In this case, emergency surgery was urgently needed, to ensure the airways and then to rebuild the destroyed areas, paying attention to the follow-up of the case because of the high risk of infection and possible complications... (AU)