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"A cephalometric survey on people of Tengger, Blitar, Kudus and Cililin people has been performed, with the aims to check their hetero/homogeneities, their affinities to mongoloid or austromelanesoid cephalometric characters, their relationship with deutero or protomalayid ancestors and their tendencies toward the presence of cephalometric characteristic polarization. The parameters measured were one stature factor, two neurocranial factor and ten splanchnocranial factors. The survey was cross sectional and on average 50 perople of each gender were sampled in all groups. The measurements were done in accordance with Martin Saller method. Age of the samples was between 18-25 years. Significant correlation of means of different groups was obtained with 'student t test', with 1% level of confidence. A significant difference was found in the head length and among the cephalic indices between Tengger people and the other groups. The cephalometric characteristics of Tengger people turned out to be closer to protomalayid than to deuteromalayid people, while the cephalometric characteristics of the other groups were closer to mongoloid than the austromelanesoid. They were homogenous without tendency of west-east polarization."

"The purpose of this study was to evaluate the changes of maxillary and mandibular sagital position relative to cranial base. The subject of this research was patients with bimaxillary protrusion having orthodontic treatment and four first premolars extraction. The maxillary and mandibular position changes were measured by angular and linier parameters. Angular parameter as described by Steiner's analysis were SNA and SNB angles. Linier parameter as described by McNamara's analysis were point A nad Pogonion relatives to nasion perpendicular. The results of both groups was compared statistically and differences quantified using paired t-test. There was no significant changes in position of maxilla relatives to cranial base. On the other hand, significant changes were found in position of mandibula relatives to cranial base."

"Latar Belakang: Pengukuran parameter Sefalometri Lateral yang menjadi bagian integral dalam penegakkan diagnosis yang selama ini dilakukan secara konvensional dengan kertas asetat dan alat tulis sebagai baku emas (gold standard), perlahan mulai digantikan dengan metode manual berbasis digital maupun dengan kecerdasan buatan yang menawarkan efisiensi dan mobilitas yang lebih baik. OneCeph™ dan WebCeph™ merupakan contoh aplikasi dan penyedia layanan tersebut. OneCeph™ merupakan aplikasi smartphone yang telah teruji akurasi dan reliabilitasnya dalam pengukuran parameter Sefalometri Lateral, sedangkan WebCeph™ merupakan aplikasi sekaligus web yang menyediakan layanan pengukuran dengan kecerdasan buatan. Hingga saat ini, belum banyak penelitian yang membandingkan secara langsung kedua metode pengukuran parameter Sefalometri Lateral yang berbeda tersebut dengan menjadikan salah satunya yang telah teruji keakuratan dan reliabilitasnya sebagai kebenaran dasar (ground truth) serta dengan menambahkan metode pengukuran berbasis kecerdasan buatan dengan koreksi manual. Tujuan: Menganalisis perbedaan hasil pengukuran parameter Sefalometri Lateral berbasis digital antara metode manual dengan kecerdasan buatan, serta kecerdasan buatan dengan koreksi manual. Metode: Sebanyak 90 Sefalogram Lateral yang memenuhi kriteria inklusi dan eksklusi sebagai sampel dilakukan pengukuran terhadap 13 parameter menggunakan kedua aplikasi dengan ketiga metode berbasis digital, yaitu metode manual, metode kecerdasan buatan, dan metode kecerdasan buatan dengan koreksi manual. Uji intraobserver dan interobserver dilakukan dengan uji Intraclass Correlation Coefficient (ICC), perbedaan hasil pengukuran antar metode dianalisis dengan uji One-Way ANOVA dan Kruskal-Wallis. Hasil: 3 dari 13 variabel yang menunjukkan perbedaan bermakna (p<0,05) pada kelompok metode kecerdasan buatan terhadap dua kelompok lain, yaitu SNA, ANB, dan U1NA (Linear). Kesimpulan: Meskipun terdapat perbedaan bermakna hasil pengukuran parameter Sefalometri Lateral antara metode kecerdasan buatan dengan dua metode lain pada beberapa variabel, tetapi potensi serta efisiensi yang ditawarkan metode berbasis kecerdasan buatan cukup baik ketika dibandingkan dengan metode manual.

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Background: The measuring Lateral Cephalometric parameters, which is an integral part of establishing a diagnosis, which has previously been carried out conventionally with acetate paper and writing instruments as the gold standard, is slowly starting to be replaced by digital-based manual methods and artificial intelligence which offer better efficiency and mobility. OneCeph™ and WebCeph™ are examples of such applications and service providers. OneCeph™ is a smartphone application that has been tested for accuracy and reliability in measuring Lateral Cephalometric parameters, while WebCeph™ is an application and web that provides measurement services with artificial intelligence. Objective: To analyze the differences in digital-based Lateral Cephalometric parameter measurement results between manual methods with artificial intelligence, and artificial intelligence with manual correction. Methods: A total of 90 lateral cephalograms that met the inclusion and exclusion criteria as samples were measured for 13 parameters using both applications with three digital-based methods, namely manual methods, artificial intelligence methods, and artificial intelligence methods with manual correction. Intraobserver and interobserver tests were carried out using the Intraclass Correlation Coefficient (ICC) test, differences in measurement results between methods were analyzed using the One-Way ANOVA and Kruskal-Wallis tests. Results: 3 of the 13 variables showed significant differences (p<0.05) in the artificial intelligence method group against the other two groups, namely SNA, ANB, and U1NA (Linear). Conclusion: Even though there are significant differences in the results of measuring Lateral Cephalometry parameters between the artificial intelligence method and the other two methods on several variables, the potential and efficiency offered by the artificial intelligence-based method is quite good when compared with the manual method."

"Latar Belakang: Pengukuran parameter Sefalometri Lateral yang menjadi bagian integral dalam penegakkan diagnosis yang selama ini dilakukan secara konvensional dengan kertas asetat dan alat tulis sebagai baku emas (gold standard), perlahan mulai digantikan dengan metode manual berbasis digital maupun dengan kecerdasan buatan yang menawarkan efisiensi dan mobilitas yang lebih baik. OneCeph™ dan WebCeph™ merupakan contoh aplikasi dan penyedia layanan tersebut. OneCeph™ merupakan aplikasi smartphone yang telah teruji akurasi dan reliabilitasnya dalam pengukuran parameter Sefalometri Lateral, sedangkan WebCeph™ merupakan aplikasi sekaligus web yang menyediakan layanan pengukuran dengan kecerdasan buatan. Hingga saat ini, belum banyak penelitian yang membandingkan secara langsung kedua metode pengukuran parameter Sefalometri Lateral yang berbeda tersebut dengan menjadikan salah satunya yang telah teruji keakuratan dan reliabilitasnya sebagai kebenaran dasar (ground truth) serta dengan menambahkan metode pengukuran berbasis kecerdasan buatan dengan koreksi manual. Tujuan: Menganalisis perbedaan hasil pengukuran parameter Sefalometri Lateral berbasis digital antara metode manual dengan kecerdasan buatan, serta kecerdasan buatan dengan koreksi manual. Metode: Sebanyak 90 Sefalogram Lateral yang memenuhi kriteria inklusi dan eksklusi sebagai sampel dilakukan pengukuran terhadap 13 parameter menggunakan kedua aplikasi dengan ketiga metode berbasis digital, yaitu metode manual, metode kecerdasan buatan, dan metode kecerdasan buatan dengan koreksi manual. Uji intraobserver dan interobserver dilakukan dengan uji Intraclass Correlation Coefficient (ICC), perbedaan hasil pengukuran antar metode dianalisis dengan uji One-Way ANOVA dan Kruskal-Wallis. Hasil: 3 dari 13 variabel yang menunjukkan perbedaan bermakna (p<0,05) pada kelompok metode kecerdasan buatan terhadap dua kelompok lain, yaitu SNA, ANB, dan U1NA (Linear). Kesimpulan: Meskipun terdapat perbedaan bermakna hasil pengukuran parameter Sefalometri Lateral antara metode kecerdasan buatan dengan dua metode lain pada beberapa variabel, tetapi potensi serta efisiensi yang ditawarkan metode berbasis kecerdasan buatan cukup baik ketika dibandingkan dengan metode manual.

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Background: The measuring Lateral Cephalometric parameters, which is an integral part of establishing a diagnosis, which has previously been carried out conventionally with acetate paper and writing instruments as the gold standard, is slowly starting to be replaced by digital-based manual methods and artificial intelligence which offer better efficiency and mobility. OneCeph™ and WebCeph™ are examples of such applications and service providers. OneCeph™ is a smartphone application that has been tested for accuracy and reliability in measuring Lateral Cephalometric parameters, while WebCeph™ is an application and web that provides measurement services with artificial intelligence. Objective: To analyze the differences in digital-based Lateral Cephalometric parameter measurement results between manual methods with artificial intelligence, and artificial intelligence with manual correction. Methods: A total of 90 lateral cephalograms that met the inclusion and exclusion criteria as samples were measured for 13 parameters using both applications with three digital-based methods, namely manual methods, artificial intelligence methods, and artificial intelligence methods with manual correction. Intraobserver and interobserver tests were carried out using the Intraclass Correlation Coefficient (ICC) test, differences in measurement results between methods were analyzed using the One-Way ANOVA and Kruskal-Wallis tests. Results: 3 of the 13 variables showed significant differences (p<0.05) in the artificial intelligence method group against the other two groups, namely SNA, ANB, and U1NA (Linear). Conclusion: Even though there are significant differences in the results of measuring Lateral Cephalometry parameters between the artificial intelligence method and the other two methods on several variables, the potential and efficiency offered by the artificial intelligence-based method is quite good when compared with the manual method."