PENGGUNAAN DEEP LEARNING UNTUK MEMPREDIKSI LUARAN FUNGSIONAL PADA STROKE ISKEMIK : TINJAUAN SISTEMATIS
DOI:
https://doi.org/10.31004/jkt.v7i1.56024Keywords:
deep learning, luaran fungsional, stroke iskemikAbstract
Stroke iskemik merupakan penyakit dengan prevalensi, mortalitas, dan morbiditas yang tinggi, sehingga ketepatan serta kecepatan prediksi luaran sangat penting dalam mendukung pengambilan keputusan klinis dan manajemen pasien. Perkembangan metode deep learning (DL) sebagai bagian dari kecerdasan buatan memanfaatkan jaringan saraf tiruan untuk mempelajari pola kompleks dari kumpulan data berskala besar secara otomatis. Pendekatan ini memungkinkan peningkatan akurasi diagnostik berbasis computer-aided diagnosis (CAD) serta prediksi luaran fungsional secara lebih presisi. Penelitian ini bertujuan mengevaluasi kinerja deep learning dalam menilai luaran fungsional pada stroke iskemik. Tinjauan sistematis dilakukan mengikuti pedoman PRISMA dengan penelusuran literatur pada periode 2015–2025. Kualitas metodologis dan risiko bias studi dinilai menggunakan instrumen ROBINS-I versi 2, sedangkan performa prediktif dianalisis berdasarkan indikator seperti Area Under the Curve (AUC), F1 Score, dan metrik relevan lainnya. Dari total 146 publikasi yang teridentifikasi, delapan studi memenuhi kriteria inklusi dengan risiko bias yang umumnya rendah. Hasil analisis menunjukkan performa prediktif yang baik, dengan nilai AUC tertinggi mencapai 0,91 dan terendah 0,71. Beberapa studi juga melaporkan akurasi yang tinggi berdasarkan F1 Score (hingga 0,79) dan nilai recall yang memadai. Secara keseluruhan, temuan ini menegaskan bahwa deep learning merupakan pendekatan yang menjanjikan dan berpotensi menjadi strategi inovatif dalam penilaian luaran fungsional pada pasien stroke iskemik.References
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