Can artificial intelligence algorithms recognize knee arthroplasty implants from X-ray radiographs?

Fatih Gölgelioğlu; Aydoğan Aşkın; Mehmet Cihat Gündoğdu; Mehmet Fatih Uzun; Bige Kağan Dedetürk; Mustafa Yalın

Makale detayları

Can artificial intelligence algorithms recognize knee arthroplasty implants from X-ray radiographs?

Yazarlar:

Fatih Gölgelioğlu | Aydoğan Aşkın | Mehmet Cihat Gündoğdu | Mehmet Fatih Uzun | Bige Kağan Dedetürk | Mustafa Yalın

Yayıncı:

MediHealth Academy Yayıncılık

Ana Başlıklar:

Ortopedi

Dergi:

Anatolian Current Medical Journal Vol.5 Issue 4, pp.479-483

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001302560

pISSN:

2718-0115

Anahtar kelimeler:

Özet:

 Aims: This study aimed to investigate the use of a convolutional neural network CNN deep learning approach to accurately identify total knee arthroplasty TKA implants from X-ray radiographs. Methods: This retrospective study employed a deep learning CNN system to analyze pre-revision and post-operative knee X-rays from TKA patients. We excluded cases involving unicondylar and revision knee replacements, as well as low-quality or unavailable X-ray images and those with other implants. Ten cruciate-retaining TKA replacement models were assessed from various manufacturers. The training set comprised 69 of the data, with the remaining 31 in the test set, augmented due to limited images. Evaluation metrics included accuracy and F1 score, and we developed the software in Python using the TensorFlow library for the CNN method. A computer scientist with AI expertise managed data processing and testing, calculating specificity, sensitivity, and accuracy to assess CNN performance. Results: In this study, a total of 282 AP and lateral X-rays from 141 patients were examined, encompassing 10 distinct knee prosthesis models from various manufacturers, each with varying X-ray counts. The CNN technique exhibited flawless accuracy, achieving a 100 identification rate for both the manufacturer and model of TKA across all 10 different models. Furthermore, the CNN method demonstrated exceptional specificity and sensitivity, consistently reaching 100 for each individual implant model. Conclusion: This study underscores the impressive capacity of deep learning AI algorithms to precisely identify knee arthroplasty implants from X-ray radiographs. It highlights AIs ability to detect subtle changes imperceptible to humans, execute precise computations, and handle extensive data. The accurate recognition of knee replacement implants using AI algorithms prior to revision surgeries promises to enhance procedure efficiency and outcomes.

 Aims: This study aimed to investigate the use of a convolutional neural network CNN deep learning approach to accurately identify total knee arthroplasty TKA implants from X-ray radiographs. Methods: This retrospective study employed a deep learning CNN system to analyze pre-revision and post-operative knee X-rays from TKA patients. We excluded cases involving unicondylar and revision knee replacements, as well as low-quality or unavailable X-ray images and those with other implants. Ten cruciate-retaining TKA replacement models were assessed from various manufacturers. The training set comprised 69 of the data, with the remaining 31 in the test set, augmented due to limited images. Evaluation metrics included accuracy and F1 score, and we developed the software in Python using the TensorFlow library for the CNN method. A computer scientist with AI expertise managed data processing and testing, calculating specificity, sensitivity, and accuracy to assess CNN performance. Results: In this study, a total of 282 AP and lateral X-rays from 141 patients were examined, encompassing 10 distinct knee prosthesis models from various manufacturers, each with varying X-ray counts. The CNN technique exhibited flawless accuracy, achieving a 100 identification rate for both the manufacturer and model of TKA across all 10 different models. Furthermore, the CNN method demonstrated exceptional specificity and sensitivity, consistently reaching 100 for each individual implant model. Conclusion: This study underscores the impressive capacity of deep learning AI algorithms to precisely identify knee arthroplasty implants from X-ray radiographs. It highlights AIs ability to detect subtle changes imperceptible to humans, execute precise computations, and handle extensive data. The accurate recognition of knee replacement implants using AI algorithms prior to revision surgeries promises to enhance procedure efficiency and outcomes.

 Aims: This study aimed to investigate the use of a convolutional neural network CNN deep learning approach to accurately identify total knee arthroplasty TKA implants from X-ray radiographs. Methods: This retrospective study employed a deep learning CNN system to analyze pre-revision and post-operative knee X-rays from TKA patients. We excluded cases involving unicondylar and revision knee replacements, as well as low-quality or unavailable X-ray images and those with other implants. Ten cruciate-retaining TKA replacement models were assessed from various manufacturers. The training set comprised 69 of the data, with the remaining 31 in the test set, augmented due to limited images. Evaluation metrics included accuracy and F1 score, and we developed the software in Python using the TensorFlow library for the CNN method. A computer scientist with AI expertise managed data processing and testing, calculating specificity, sensitivity, and accuracy to assess CNN performance. Results: In this study, a total of 282 AP and lateral X-rays from 141 patients were examined, encompassing 10 distinct knee prosthesis models from various manufacturers, each with varying X-ray counts. The CNN technique exhibited flawless accuracy, achieving a 100 identification rate for both the manufacturer and model of TKA across all 10 different models. Furthermore, the CNN method demonstrated exceptional specificity and sensitivity, consistently reaching 100 for each individual implant model. Conclusion: This study underscores the impressive capacity of deep learning AI algorithms to precisely identify knee arthroplasty implants from X-ray radiographs. It highlights AIs ability to detect subtle changes imperceptible to humans, execute precise computations, and handle extensive data. The accurate recognition of knee replacement implants using AI algorithms prior to revision surgeries promises to enhance procedure efficiency and outcomes.

 Aims: This study aimed to investigate the use of a convolutional neural network CNN deep learning approach to accurately identify total knee arthroplasty TKA implants from X-ray radiographs. Methods: This retrospective study employed a deep learning CNN system to analyze pre-revision and post-operative knee X-rays from TKA patients. We excluded cases involving unicondylar and revision knee replacements, as well as low-quality or unavailable X-ray images and those with other implants. Ten cruciate-retaining TKA replacement models were assessed from various manufacturers. The training set comprised 69 of the data, with the remaining 31 in the test set, augmented due to limited images. Evaluation metrics included accuracy and F1 score, and we developed the software in Python using the TensorFlow library for the CNN method. A computer scientist with AI expertise managed data processing and testing, calculating specificity, sensitivity, and accuracy to assess CNN performance. Results: In this study, a total of 282 AP and lateral X-rays from 141 patients were examined, encompassing 10 distinct knee prosthesis models from various manufacturers, each with varying X-ray counts. The CNN technique exhibited flawless accuracy, achieving a 100 identification rate for both the manufacturer and model of TKA across all 10 different models. Furthermore, the CNN method demonstrated exceptional specificity and sensitivity, consistently reaching 100 for each individual implant model. Conclusion: This study underscores the impressive capacity of deep learning AI algorithms to precisely identify knee arthroplasty implants from X-ray radiographs. It highlights AIs ability to detect subtle changes imperceptible to humans, execute precise computations, and handle extensive data. The accurate recognition of knee replacement implants using AI algorithms prior to revision surgeries promises to enhance procedure efficiency and outcomes.

Makale detayları

Can artificial intelligence algorithms recognize knee arthroplasty implants from X-ray radiographs?

Yazarlar:

Fatih Gölgelioğlu | Aydoğan Aşkın | Mehmet Cihat Gündoğdu | Mehmet Fatih Uzun | Bige Kağan Dedetürk | Mustafa Yalın

Yayıncı:

MediHealth Academy Yayıncılık

Ana Başlıklar:

Ortopedi

Dergi:

Anatolian Current Medical Journal Vol.5 Issue 4, pp.479-483

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001302560

pISSN:

2718-0115

Artroplasty

implant

artificial intelligence

detection

Sayı

Makale detayları

Can artificial intelligence algorithms recognize knee arthroplasty implants from X-ray radiographs?

Yazarlar: Fatih Gölgelioğlu | Aydoğan Aşkın | Mehmet Cihat Gündoğdu | Mehmet Fatih Uzun | Bige Kağan Dedetürk | Mustafa Yalın

Yayıncı: MediHealth Academy Yayıncılık

Ana Başlıklar: Ortopedi

Dergi: Anatolian Current Medical Journal Vol.5 Issue 4, pp.479-483

Belge Tipi: Makale

Yayın Tarihi: 10/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001302560

pISSN: 2718-0115

Artroplasty

implant

artificial intelligence

detection

Sayı

Yazarlar:

Fatih Gölgelioğlu | Aydoğan Aşkın | Mehmet Cihat Gündoğdu | Mehmet Fatih Uzun | Bige Kağan Dedetürk | Mustafa Yalın

Yayıncı:

MediHealth Academy Yayıncılık

Ana Başlıklar:

Ortopedi

Dergi:

Anatolian Current Medical Journal Vol.5 Issue 4, pp.479-483

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001302560

pISSN:

2718-0115