POTENSI KECERDASAN BUATAN DALAM PENINGKATAN AKURASI PEMBACAAN HASIL MAMOGRAFI: TINJAUAN SISTEMATIS DAN META-ANALISIS

AL MUNAWIR; Sekar Arum Srigati; Pipiet Wulandari

Authors

AL MUNAWIR Laboratorium Patologi Anatomi FK Universitas Jember
Sekar Arum Srigati Fakultas Kedokteran, Universitas Jember
Pipiet Wulandari Departemen Fisiologi, Fakultas Kedokteran, Universitas Jember

Keywords:

AI, deep learning, kanker payudara, mamografi

Abstract

ABSTRAK

Kanker payudara merupakan suatu penyakit keganasan oleh karena proliferasi tak terkontrol dari sel-sel di payudara. Jumlah morbiditas dan mortalitas yang cukup tinggi menjadikan upaya skrining dan deteksi dini kanker payudara penting untuk dilakukan. Mamografi merupakan modalitas utama skrining kanker payudara yang diinterpretasikan oleh ahli radiologi berdasarkan persepsi visual. Namun, peningkatan permintaan skrining selaras dengan peningkatan beban kerja yang dapat mempengaruhi efektivitas dan menyebabkan kesalahan interpretasi hasil mamografi. Perkembangan teknologi, salah satunya kecerdasan buatan (AI) dengan algoritma deep learning diklaim memiliki kinerja yang lebih baik daripada kinerja rata-rata ahli radiologi. Tujuan penelitian ini adalah untuk mengkaji potensi AI dalam meningkatkan akurasi pembacaan hasil mamografi. Penelitian ini merupakan tinjauan sistematis dan meta-analisis menggunakan artikel dengan desain penelitian retrospektif dari lima basis data sesuai panduan Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Risiko bias dikaji menggunakan Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Total terdapat 12 artikel terinklusi yang dianalisis berdasarkan penggunaan tunggal AI, ahli radiologi, dan kombinasi ahli radiologi-AI. Hasil meta-analisis penggunaan tunggal AI menunjukkan hasil yang lebih tinggi dibandingkan lainnya pada sensitivitas (88% (95% CI 82%-92%)), spesifisitas (89% (95% CI 81%-93%)), dan AUC (0,94 (95% CI 0,92-0,96)). Penelitian ini menunjukkan adanya potensi yang menjanjikan dari kecerdasan buatan (AI) untuk meningkatkan akurasi pembacaan hasil mamografi.

Kata Kunci: AI, deep learning, kanker payudara, mamografi

ABSTRACT

Breast cancer is a malignancy caused by the uncontrollable proliferation of breast cells. The high morbidity and mortality make an essential excuse for screening and early detection of breast cancer. Mammography is the main modality in the examination of breast cancer screening which is interpreted by radiologists based on visual perception. The increase in screening demand leads to workload which affects the effectiveness and misinterpretation of mammography results. These years, technological development such as artificial intelligence (AI) in its deep learning algorithm claimed to have better performance than the average performance of radiologists. Hence, this study aimed to investigate the potency of AI to enhance the accuracy of a mammography reading. This systematic review and meta-analysis conducted retrospective articles from five electronic databases based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The risk of biases was assessed from each study using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Twelve articles were included and analyzed for the AI stand-alone, radiologists stand-alone, and combination of radiologists with AI. The current study showed the higher results of AI stand-alone compared to others in its sensitivity (88% (95% CI 82%-92%)), specificity (89% (95% CI 81%-93%)), and area under the curves (0,94 (95% CI 0,92-0,96)). In conclusion, this systematic review and meta-analysis provide valuable evidence about AI's promising potency to enhance mammography reading accuracy.

Keywords: AI, breast cancer, deep learning, mammography

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