Application of Artificial Intelligence (AI) in a Cephalometric Analysis: A Narrative Review

Kiełczykowski, Michał; Kamiński, Konrad; Perkowski, Konrad; Zadurska, Małgorzata; Czochrowska, Ewa

doi:10.3390/diagnostics13162640

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…Такі медичні програми з використанням штучного інтелекту стають все більш поширеними [3,4]. У літературі є різні дослідження порівняння різноманітних програм для комп'ютерного цефалометричного аналізу та з використанням звичайного методу.…”

Section: дане дослідження є фрагментом ндр «особливості реабілітації ...unclassified

Contemporary Applications of Computer Technologies in Orthodontics

Vyzhenko

2023

Act. Probl. of the Modern Med.

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Cephalometric X-ray image analysis is part of diagnostic procedures in dento-maxillofacial orthopedics and orthodontics. The integration of artificial intelligence (AI) into medical programs is increasingly prevalent, with several studies comparing various computerized cephalometric analysis programs against traditional methods. However, no definitive consensus has been reached on the ideal methodology. In the realm of cephalometric analysis, errors can arise in image acquisition, identification, and measurement. Currently, there are many different programs for calculating and analyzing X-ray images of the head. One of them is AudaxCeph, a program enabling clinicians to incorporate specialized knowledge into computer software and apply it for cephalometric analysis of X-ray images.This study delves into the landscape of cephalometric analysis programs, with a specific focus on AudaxCeph. AudaxCeph stands out as a program that allows the incorporation of specialist knowledge into computer software for cephalometric analysis of X-ray images. Through the collaboration between Poltava State Medical University and "Audax doo" in Ljubljana, Slovenia, the AudaxCeph program has been integrated into medical, advisory, scientific, and educational practices at the department of postgraduate education for orthodontists.At the department of postgraduate education of orthodontists of the Poltava State Medical University, according to the cooperation agreement between the university and "Audax doo" company Ljubljana, Slovenia, the AudaxCeph program is used in medical and advisory, scientific and educational work. The use of the program for cephalometric analysis is implemented in the educational process, in particular, in the curricula and programs of the specialty "Orthodontics" for interns, thematic improvement cycles, and in specialization cycle in the "Diagnostics in Orthodontics" section. The program was also used to conduct a number of scientific studies on the diagnosis of malocclusion. Conclusions. The implementation of AudaxCeph in educational processes, such as internships, thematic improvement cycles, and specialization cycles in the "Diagnostics in Orthodontics" section, demonstrates its versatile utility. Moreover, the program has been instrumental in conducting scientific studies on malocclusion diagnosis. In conclusion, while the use of computer programs enhances the efficiency of orthodontists, the role of artificial intelligence should be viewed as a supplementary diagnostic tool rather than the sole determinant of accurate analysis.

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Section: дане дослідження є фрагментом ндр «особливості реабілітації ...unclassified

Contemporary Applications of Computer Technologies in Orthodontics

Vyzhenko

2023

Act. Probl. of the Modern Med.

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“…They are essential for improving the interpretation of pictures from many modalities, including PET scans and ultrasounds. Algorithms for automated categorization and feature extraction assist in locating areas of interest and offer crucial data for precise diagnosis [25]. This is especially helpful in the fields of neurology, cardiovascular medicine, and oncology.…”

Section: Medical Imagingmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Diagnostics and Treatment Planning

Tak

2023

J Arti Inte & Cloud Comp

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This paper explores how machine learning (ML) and artificial intelligence (AI) are transforming treatment planning and diagnosis in the healthcare industry. These technologies, which make use of sophisticated algorithms and computer models, have shown great promise for improving the precision, effectiveness, and customized nature of medical therapies. When using AI and ML for diagnostics, large datasets from patient records to medical images must be analyzed. These technologies facilitate prevention and treatment by enabling rapid and exact illness identification through deep learning and pattern recognition algorithms. Predictive modeling also makes it possible to anticipate how a disease will progress, which makes preemptive and customized treatment plans possible. AI and ML play a major role in optimizing therapeutic techniques during treatment planning. These technologies aid in the development of the best treatment plans based on distinct responses, genetic characteristics, and other pertinent aspects by evaluating data specific to each patient. This promotes a more a patient-focused healthcare paradigm by minimizing side effects and increasing therapeutic efficacy. The study also looks at the difficulties and moral issues surrounding the application of artificial intelligence and machine learning to medicine. Notwithstanding the encouraging results, it is crucial to underline the necessity for strong validation, openness, and responsible technology deployment in order to guarantee these technologies' moral and trustworthy use in healthcare contexts. In summary, the combination of AI and ML has enormous potential to transform treatment planning and diagnosis, presenting hitherto unheard-of chances for precision medicine and better patient outcomes. As these technologies develop further, the way they fit into clinical workflows might completely change the way healthcare is delivered and usher in a new era of tailored, data-driven treatments.

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“…A significant number of studies reported a confidence interval within a range of 2 mm, with the mean percentage of accurately identified landmarks being over 80%. Nevertheless, in terms of clinical assessment, a localization error of up to 2 mm may be deemed acceptable for certain sites in a cephalometric examination, but not for all [11]. Precise localization of cephalometric sites A and B in the horizontal plane is essential for determining the maxillary and mandibular relations in the sagittal plane.…”

Section: Introductionmentioning

confidence: 99%

Radiological Measurement of Sella Turcica Dimensions in Different Skeletal Patterns in Romanian Patients

Szuhanek,

Uzun,

Galuscan

et al. 2024

Applied Sciences

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(1) Background: The purpose of this study was to evaluate the morphology and linear dimensions of sella turcica in Romanian participants from all three skeletal classes to see whether there were any differences. (2) Method: We examined 90 lateral cephalometric radiographs of patients aged 12 and older and divided them into skeletal classes I, II, and III (30 participants in each). Sella turcica linear measurements such as length, depth, and anteroposterior diameter were measured and studied. To see the nature of our data, Q–Q plots tests were performed. By examining these tests performed for each variable belonging to a particular class, it can be noted that the points are fairly well distributed along some lines, meaning that the data are normally distributed. An Anova test with Bonferroni correction was used to compare the mean values of the examined parameters between the classes. Also, to observe the correlation between our experimental data, the Pearson correlation coefficient was calculated. (3) Results: In all three skeletal classes, the average length of the sella was 8.98 mm ± 1.470, the average depth was 7.99 mm ± 1.081, and the average diameter was 10.29 mm ± 1.267. For all examined linear dimensions, there was a statistically significant difference between class I and class III subjects and between class II and class III subjects (p < 0.001). The morphology of sella turcica was found to be normal in 51.1% of instances, representing the majority across all skeletal classes. In the Romanian population, sella turcica has shown a significant amount of variation. Class III subjects had larger sella dimensions, whereas class II subjects had smaller values. (4) Conclusions: The measurements and morphology of the sella analysed in the present research can serve as standards for subsequent research concerning the sella turcica region in individuals from Romania.

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Application of Artificial Intelligence (AI) in a Cephalometric Analysis: A Narrative Review

Cited by 10 publications

References 35 publications

Contemporary Applications of Computer Technologies in Orthodontics

Contemporary Applications of Computer Technologies in Orthodontics

Artificial Intelligence and Machine Learning in Diagnostics and Treatment Planning

Radiological Measurement of Sella Turcica Dimensions in Different Skeletal Patterns in Romanian Patients

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