Metrisor: a novel diagnostic method for metritis detection in cattle based on machine learning and sensors
Yazarlar (11)
Prof. Dr. Ali Rışvanlı Fırat Üniversitesi, Türkiye
Doç. Dr. Burak Tanyeri Fırat Üniversitesi, Türkiye
Doç. Dr. Güngör Yıldırım Fırat Üniversitesi, Türkiye
Yetkin Tatar
Firat Üniversitesi, Türkiye
Firat Üniversitesi, Türkiye
Mehmet Gedikpınar
Fırat Üniversitesi, Türkiye
Fırat Üniversitesi, Türkiye
Prof. Dr. Hakan Kalender Firat Üniversitesi, Türkiye
Doç. Dr. Tarık ŞAFAK Kastamonu Üniversitesi, Türkiye
Öğr. Gör. Burak Yüksel Firat Üniversitesi, Türkiye
Dr. Öğr. Üyesi Burcu Karagülle Firat Üniversitesi, Türkiye
Öznur Yılmaz
Siirt Üniversitesi, Türkiye
Siirt Üniversitesi, Türkiye
Dr. Öğr. Üyesi Mehmet Akif Kılınç University Of Bingöl, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Theriogenology (Q1) | ||
| Dergi ISSN | 0093-691X Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 05-2024 |
| Cilt / Sayı / Sayfa | 223 / 1 / 115–121 | DOI | 10.1016/j.theriogenology.2024.05.002 |
| Makale Linki | http://dx.doi.org/10.1016/j.theriogenology.2024.05.002 | ||
| Özet |
| The Metrisor device has been developed using gas sensors for rapid, highly accurate and effective diagnosis of metritis. 513 cattle uteri were collected from abattoirs and swabs were taken for microbiological testing. The Metrisor device was used to measure intrauterine gases. The results showed a bacterial growth rate of 75.75 % in uteri with clinical metritis. In uteri positive for clinical metritis, the most commonly isolated and identified bacteria were Trueperella pyogenes, Fusobacterium necrophorum and Escherichia coli. Measurements taken with Metrisor to determine the presence of metritis in the uterus yielded the most successful results in evaluations of relevant machine learning algorithms. The ICO (Iterative Classifier Optimizer) algorithm achieved 71.22 % accuracy, 64.40 % precision and 71.20 % recall. Experiments were conducted to examine bacterial growth in the uterus and the random forest algorithm produced the most successful results with accuracy, precision and recall values of 78.16 %, 75.30 % and 78.20 % respectively. ICO also showed high performance in experiments to determine bacterial growth in metritis-positive uteri, with accuracy, precision and recall values of 78.97 %, 77.20 % and 79.00 %, respectively. In conclusion, the Metrisor device demonstrated high accuracy in detecting metritis and bacterial growth in uteri and could identify bacteria such as E. coli, S. aureus, coagulase-negative staphylococci, T. pyogenes, Bacillus spp., Clostridium spp. and F. necrophorum with rates up to 80 %. It provides a reliable, rapid and effective means of detecting metritis in animals in the field without the need for laboratory facilities. |
| Anahtar Kelimeler |
| Cow | Machine learning | Metrisor | Metritis | Sensor |
Science Direct
Metrisor: a novel diagnostic method for metritis detection in cattle based on machine learning and sensors
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 5 |
| Scopus | 5 |