Perkembangan Ilmu Histologi Hingga Masa Artificial Intelligence Pada Darah Secara Normal Maupun Abnormal/Patologis

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Diterbitkan Jun 2, 2026
https://doi.org/10.30651/ps.v5i1.31742
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Vol 5 No 1 (2026): Proceeding Series LSPID UMSURA

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Roro Dewi Sekar Ayu Wicaksono
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Ruwaidah Khoirun Niswah
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Raja Ibrahim Ramadhan W
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Fazrin Rahma Anggrainy
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Zuhro Aribatun Rosydah
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Ragil Nurmuhammad B
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Arras Puspawati
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Lega Rahmadani
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Aninnur Qotrunnada F.Z
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Allief Rahman
Fakultas Kedokteran Universitas Muhammadiyah Surabaya
Nur Mujaddidah Mochtar
Departemen Histologi, Fakultas Kedokteran Universitas Muhammadiyah Surabaya

Abstrak

Perkembangan histologi sel darah dengan menggunakan teknik apusan darah tepi yang berlangsung sejak abad ke-18 hingga di zaman artificial intelligence (AI) memberikan dampak yang signifikan terhadap perkembangan ilmu kedokteran klinik dalam menganalisis morfologi abnormal/kelainan sel darah atau histopatologi darah. Tujuan dari jurnal ini, untuk mengkaji perkembangan teknologi dalam identifikasi morfologi sel darah, normal dan abnormal/patologis, hingga pada masa AI. Dengan menggunakan metode analisis beberapa jurnal yang berkaitan dengan modernisasi teknologi hemato-histopatologi, didapatkan beberapa penemuan antara lain, Generative Adversarial Networks (GAN) yang mampu mengatasi perbedaan pewarnaan, Cella Vision Proficiency Software yang merupakan sebuah sistem digital untuk menjamin kesesuaian klasifikasi sel darah, teknologi Convolutional Neural Networks (CNN) memungkinkan proses pembelajaran karakteristik sel dilakukan secara otomatis dan mendalam, serta CLARITY dan metode serupa yang memungkinkan visualisasi jaringan utuh tanpa perlu pemotongan berulang.

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