Researcher:
Karagöz, Ahmet Agah

Profile Picture
ORCID

Job Title

PhD Student

First Name

Ahmet Agah

Last Name

Karagöz

Name

Name Variants

Karagöz, Ahmet Agah

Email Address

Birth Date

Filters

20221

Advanced Search

Filter by

Settings

Researcher Of Publications: search.filters.isAuthorOfPublication.c0d2b4e2-5693-492a-ba23-9285526f17aa

Search Results

Now showing 1 - 1 of 1
  • Placeholder
    PublicationMetadata only
    Machine learning-enabled prediction of 3D-printed microneedle features
    (Mdpi, 2022) Alseed, M. Munzer; N/A; N/A; Department of Mechanical Engineering; Sarabi, Misagh Rezapour; Karagöz, Ahmet Agah; Taşoğlu, Savaş; PhD Student; PhD Student; Faculty Member; Department of Mechanical Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 291971
    Microneedles (MNs) introduced a novel injection alternative to conventional needles, offering a decreased administration pain and phobia along with more efficient transdermal and intradermal drug delivery/sample collecting. 3D printing methods have emerged in the field of MNs for their time- and cost-efficient manufacturing. Tuning 3D printing parameters with artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is an emerging multidisciplinary field for optimization of manufacturing biomedical devices. Herein, we presented an AI framework to assess and predict 3D-printed MN features. Biodegradable MNs were fabricated using fused deposition modeling (FDM) 3D printing technology followed by chemical etching to enhance their geometrical precision. DL was used for quality control and anomaly detection in the fabricated MNAs. Ten different MN designs and various etching exposure doses were used create a data library to train ML models for extraction of similarity metrics in order to predict new fabrication outcomes when the mentioned parameters were adjusted. The integration of AI-enabled prediction with 3D printed MNs will facilitate the development of new healthcare systems and advancement of MNs' biomedical applications.