Gediz GEDUK, Hatice BİLTEKİN, Çiğdem ŞEKER

PANORAMİK RADYOGRAFLARDA APİKAL PATOLOJİ TEŞHİSİ İÇİN YAPAY ZEKA YETERLİ GÜVENİLİRLİK DÜZEYİNE ULAŞTI MI: FARKLI EŞİK DEĞERLERDE KARŞILAŞTIRMALI ÇALIŞMA

Amaç: Panoramik radyografiler üzerinden tespit edilen apikal patolojilerin yapay zekâ temelli ticari bir yazılım ile doğruluğunun tespit edilmesidir. Gereç ve yöntem: En az bir tane apikal patolojinin olduğu 100 panoramik radyograf seçilmiş ve Denti.AI ticari yazılımına yüklenmiştir. Sistemin %30, %60, %90 eşik değerlerinde apikal patolojiyi tespit etmesi sağlanmıştır. Panoramik radyografiler Ağız, Diş ve Çene Radyolojisi alanında uzmanlığını almış iki hekim ve Ağız, Diş ve Çene Radyolojisi uzmanlık eğitimini sürdüren iki araştırma görevlisi tarafından seçilmiştir. Verilerin analizi SPSS 22.0 Paket Veri Programı ile yapılmıştır. Bulgular: Çalışmanın sonunda %30, %60, %90 eşik değerleri için sensivite değerleri sırasıyla %62.6, %38.1 ve %6.8, spesifite değerleri %0, %100 ve %100, tanısal doğruluk değerleri 61.3, 39.3 ve 8.6 olarak bulunmuştur. PPV değerleri %96.8, 100 ve 100, NPV değerleri 0, 3.2 ve 2.1, AUC değerleri ise 0.313, 0.69 ve 0.534 olarak hesaplanmıştır. İstatistiki testlerde p<0.05 değeri anlamlı olarak kabul edilmiştir. Sonuç: Yapay zekâ programı, özellikle eşik değer düşürüldüğü zaman uzman hekimlere yakın sonuçlar vermektedir. Bu şekilde hazırlanan yapay zekâ tabanlı ticari yazılımlarda örnek sayılarının artışı ve bunların geriye dönük tespit edilmesinin doğruluğu artıracağını, bu tip yazılımların klinik tanılarda daha çok yer alacağını ve yoğun kliniklerde başvurulabilecek bir destek sistemi olabileceğini düşünmekteyiz.

Anahtar Kelimeler:

apikal patoloji, derin öğrenme, panoramik radyografi, yapay zeka

RELIABILITY ASSESSMENT OF ARTIFICIAL INTELLIGENCE FOR THE DIAGNOSIS OF APICAL PATHOLOGY IN PANORAMIC RADIOGRAPHS: A COMPARATIVE STUDY AT DIFFERENT THRESHOLD VALUES

Background: To determine the accuracy of apical pathologies detected on panoramic radiographs with an artificial intelligence-based commercial software. Methods: 100 panoramic radiographs with at least one apical pathology were selected and uploaded to Denti.AI commercial software. The system was enabled to detect apical pathology at 30%, 60%, and 90% threshold values. Panoramic radiographs were selected by two Oral and Maxillofacial Radiologists and two research assistants who are continuing their residency training in Oral and Maxillofacial Radiology. Data analysis was done with SPSS 22.0 Packet Data Program. Results: At the end of the study, sensitivity values were 62.6%, 38.1% and 6.8%, specificity values were 0%, 100% and 100%, and diagnostic accuracy values were 61.3, 39.3 and 8.6 for the threshold values of 30%, 60%, 90%, respectively. PPV values were calculated as 96.8, 100 and 100%, NPV values as 0, 3.2 and 2.1, AUC values as 0.313, 0.69 and 0.534. In statistical tests, p <0.05 value was accepted as significant. Conclusion: The artificial intelligence program gives results close to specialist dentists, especially when the threshold value is lowered. We think that the increase in the number of samples and retrospective detection of artificial intelligence-based commercial software prepared in this way will increase the accuracy, this type of software will be more involved in clinical diagnoses and can be a support system that can be used in intensive clinics.

Keywords:

apical pathology, artificial intelligence, deep learning, panoramic radiography,

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