Panorami̇k Radyograflarda Api̇kal Patoloji̇ Teşhi̇si̇ İçi̇n Yapay Zeka Yeterli̇ Güveni̇li̇rli̇k Düzeyi̇ne Ulaşti Mi: Farkli Eşi̇k Değerlerde Karşilaştirmali Çalişma

Geduk, Gediz; BİLTEKİN, Hatice; Şeker, Çiğdem

doi:10.15311/selcukdentj.835913

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…They stated that the AI software obtains results close to specialist dentists, and increases in the number of samples in AI-based commercial software prepared in this way. Retrospective detection would increase the accuracy, and this type of software would be more involved in clinical diagnosis ( 24 ). In this study, specialist dentists’ perceptions were set as the ground truth, and the accuracy of detecting periapical infection was compared among new AI software and junior dentists.…”

Section: Discussionmentioning

confidence: 99%

Comparison of artificial intelligence vs. junior dentists’ diagnostic performance based on caries and periapical infection detection on panoramic images

Güneç,

Ürkmez,

Danaci

et al. 2023

Quant Imaging Med Surg

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Background There is information missing in the literature about the comparison of dentists vs. artificial intelligence (AI) based on diagnostic capability. The aim of this study is to evaluate the diagnostic performance based on radiological diagnoses regarding caries and periapical infection detection by comparing AI software with junior dentists who have 1 or 2 years of experience, based on the valid determinations by specialist dentists. Methods In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an AI software performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds. Results The AI and the three junior dentists, respectively, detected dental caries at a sensitivity (SEN) of 0.907, 0.889, 0.491, 0.907; a specificity (SPEC) of 0.760, 0.740, 0.454, 0.696; a positive predictive value (PPV) of 0.693, 0.470, 0.155, 0.666; a negative predictive value (NPV) of 0.505, 0.415, 0.275, 0.367 and a F1-score of 0.786, 0.615, 0.236, 0.768. The AI and the three junior dentists respectively detected periapical lesions at an SEN of 0.973, 0.962, 0.758, 0.958; a SPEC of 0.629, 0.421, 0.404, 0.621; a PPV of 0.861, 0.651, 0.312, 0.648; a NPV of 0.689, 0.673, 0.278, 0.546 and an F1-score of 0.914, 0.777, 0.442, 0.773. The AI software gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both AI and junior dentists. Conclusions Regarding the evaluation time needed, AI performed faster, on average.

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Section: Discussionmentioning

confidence: 99%

Comparison of artificial intelligence vs. junior dentists’ diagnostic performance based on caries and periapical infection detection on panoramic images

Güneç,

Ürkmez,

Danaci

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…They stated that the arti cial intelligence program obtains results close to specialist dentists, and increases in the number of samples in arti cial intelligence-based commercial software prepared in this way. Retrospective detection would increase the accuracy, and this type of software would be more involved in clinical diagnosis [24]. In this study, specialist dentists' perceptions were set as the ground truth, and the accuracy of detecting periapical infection was compared among new arti cial intelligence application and junior dentists.…”

Section: Discussionmentioning

confidence: 99%

Success of Caries and Periapical Infection Diagnoses on Panoramic images: Artificial Intelligence vsJunior and Specialist Dentists

Güneç

2023

Preprint

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Purpose: The aim of this study is to evaluate the success rate of radiological diagnoses regarding caries and periapical infection, comparing an artificial intelligence application against junior dentists, based on the valid determinations by specialist dentists. Methods: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an artificial intelligence application performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds. Results: The artificial intelligence and the three junior dentists, respectively, detected dental caries at an SEN of 0.907,0.889,0.491,0.907; a SPEC of 0.760,0.740,0.454,0.769660; a PPV of 0.693,0.470,0.155,0.666; an NPV of 0.505,0.415,0.275,0.367 and an F1-score of 0.786,0.615,0.236,0.768. The artificial intelligence and the three junior dentists respectively detected periapical lesions at an SEN of 0.973,0.962,0.758,0.958; a SPEC of 0.629,0.421,0.404,0.621; a PPV of 0.861,0.651,0.312,0.648; an NPV of 0.689,0.673,0.278,0.546 and an F1-score of 0.914,0.777,0.442,0.773. Conclusion: The artificial intelligence application gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both artificial intelligence and junior dentists. Regarding the evaluation time needed, artificial intelligence performed faster, on average.

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Success of Caries and Periapical Infection Diagnoses on Panoramic images: Artificial Intelligence vs Junior and Specialist Dentists

Güneç

2023

Preprint

View full text Add to dashboard Cite

Objectives The aim of this study is to evaluate the success rate of radiological diagnoses regarding caries and periapical infection, comparing an artificial intelligence application against junior dentists, based on the valid determinations by specialist dentists.Methods In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an artificial intelligence application performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds.Results The artificial intelligence and the three junior dentists, respectively, detected dental caries at an SEN of 0.907,0.889,0.491,0.907; a SPEC of 0.760,0.740,0.454,0.769660; a PPV of 0.693,0.470,0.155,0.666; an NPV of 0.505,0.415,0.275,0.367 and an F1-score of 0.786,0.615,0.236,0.768. The artificial intelligence and the three junior dentists respectively detected periapical lesions at an SEN of 0.973,0.962,0.758,0.958; a SPEC of 0.629,0.421,0.404,0.621; a PPV of 0.861,0.651,0.312,0.648; an NPV of 0.689,0.673,0.278,0.546 and an F1-score of 0.914,0.777,0.442,0.773.Conclusion The artificial intelligence application gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both artificial intelligence and junior dentists. Regarding the evaluation time needed, artificial intelligence performed faster, on average.

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Panorami̇k Radyograflarda Api̇kal Patoloji̇ Teşhi̇si̇ İçi̇n Yapay Zeka Yeterli̇ Güveni̇li̇rli̇k Düzeyi̇ne Ulaşti Mi: Farkli Eşi̇k Değerlerde Karşilaştirmali Çalişma

Cited by 3 publications

References 27 publications

Comparison of artificial intelligence vs. junior dentists’ diagnostic performance based on caries and periapical infection detection on panoramic images

Comparison of artificial intelligence vs. junior dentists’ diagnostic performance based on caries and periapical infection detection on panoramic images

Success of Caries and Periapical Infection Diagnoses on Panoramic images: Artificial Intelligence vsJunior and Specialist Dentists

Success of Caries and Periapical Infection Diagnoses on Panoramic images: Artificial Intelligence vs Junior and Specialist Dentists

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