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Soydan Z, Saglam Y, Key S, Kati YA, Taskiran M, Kiymet S, Salturk T, Aydin AS, Bilgili F, Sen C. An AI based classifier model for lateral pillar classification of Legg-Calve-Perthes. Sci Rep 2023; 13:6870. [PMID: 37106026 PMCID: PMC10140055 DOI: 10.1038/s41598-023-34176-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 04/25/2023] [Indexed: 04/29/2023] Open
Abstract
We intended to compare the doctors with a convolutional neural network (CNN) that we had trained using our own unique method for the Lateral Pillar Classification (LPC) of Legg-Calve-Perthes Disease (LCPD). Thousands of training data sets are frequently required for artificial intelligence (AI) applications in medicine. Since we did not have enough real patient radiographs to train a CNN, we devised a novel method to obtain them. We trained the CNN model with the data we created by modifying the normal hip radiographs. No real patient radiographs were ever used during the training phase. We tested the CNN model on 81 hips with LCPD. Firstly, we detected the interobserver reliability of the whole system and then the reliability of CNN alone. Second, the consensus list was used to compare the results of 11 doctors and the CNN model. Percentage agreement and interobserver analysis revealed that CNN had good reliability (ICC = 0.868). CNN has achieved a 76.54% classification performance and outperformed 9 out of 11 doctors. The CNN, which we trained with the aforementioned method, can now provide better results than doctors. In the future, as training data evolves and improves, we anticipate that AI will perform significantly better than physicians.
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Affiliation(s)
- Zafer Soydan
- Orthopedics and Traumatology, Bhtclinic İstanbul Tema Hastanesi, Nisantası University, Atakent Mh 4. Cadde No 36 PC, 34307, Kucukcekmece, Istanbul, Turkey.
| | - Yavuz Saglam
- Orthopedics and Traumatology, Istanbul University Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Sefa Key
- Orthopedics and Traumatology, Bingol State Hospital, Bingol Merkez, Turkey
| | - Yusuf Alper Kati
- Orthopedics and Traumatology, Antalya Egitim ve Arastirma Hastanesi, Antalya, Turkey
| | - Murat Taskiran
- Department of Electronics and Communication Engineering, Yildiz Technical University, Istanbul, Turkey
| | - Seyfullah Kiymet
- Department of Electronics and Communication Engineering, Yildiz Technical University, Istanbul, Turkey
| | - Tuba Salturk
- Department of Informatics, Yildiz Technical University, Istanbul, Turkey
| | - Ahmet Serhat Aydin
- Orthopedics and Traumatology, Istanbul University Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Fuat Bilgili
- Orthopedics and Traumatology, Istanbul University Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Cengiz Sen
- Orthopedics and Traumatology, Istanbul University Istanbul Faculty of Medicine, Istanbul, Turkey
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A Symmetry-Based Superposition Method for Planning and Surgical Outcome Assessment. Bioengineering (Basel) 2023; 10:bioengineering10030335. [PMID: 36978726 PMCID: PMC10045002 DOI: 10.3390/bioengineering10030335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 03/09/2023] Open
Abstract
Computer-aided surgical planning has been widely used to increase the safety and predictability of surgery. The validation of the target of surgical planning to surgical outcomes on a patient-specific model is an important issue. The aim of this research was to develop a robust superposition method to assess the deviation of planning and outcome by using the symmetrical characteristic of the affected target. The optimal symmetry plane (OSP) of an object is usually used to evaluate the degree of symmetry of an object. We proposed a refined OSP-based contouring method to transfer a complex three-dimensional superposition operation into two dimensions. We compared the typical iterative closest point (ICP) algorithm with the refined OSP-based contouring method and examined the differences between them. The results using the OSP-based method were much better than the traditional method. As for processing time, the OSP-based contouring method was 11 times faster than the ICP method overall. The proposed method was not affected by the metallic artifacts from medical imaging or geometric changes due to surgical intervention. This technique can be applied for post-operative assessment, such as quantifying the differences between surgical targets and outcomes as well as performing long-term medical follow-up.
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Deep learning with multiresolution handcrafted features for brain MRI segmentation. Artif Intell Med 2022; 131:102365. [DOI: 10.1016/j.artmed.2022.102365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 06/28/2022] [Accepted: 07/09/2022] [Indexed: 12/26/2022]
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Memiş A, Varlı S, Bilgili F. Fast and Accurate Registration of the Proximal Femurs in Bilateral Hip Joint Images by Using the Random Sub-Sample Points. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2021.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Image based quantification of the proximal femur shape deformities in 3D by using the contralateral healthy shape structure: A preliminary study. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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