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The radiographs were independently classified into 6 stages by a radiologist using their simultaneous MRIs. After that, the radiographs were given to train and test the deep learning models of the project including SVM and ANFIS layer using the Python programming language and TensorFlow library. In the last step, the test set of hip radiographs was provided to two independent radiologists with different work experiences to compare their diagnosis performance to the deep learning models\u2019 performance using the F1 score and Mcnemar test analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The performance of SVM for AVNFH detection (AUC\u2009=\u200982.88%) was slightly higher than less experienced radiologists (79.68%) and slightly lower than experienced radiologists (88.4%) without reaching significance (p-value\u2009&gt;\u20090.05). Evaluation of the performance of SVM for pre-collapse AVNFH detection with an AUC of 73.58% showed significantly higher performance than less experienced radiologists (AUC\u2009=\u200960.70%, p-value\u2009&lt;\u20090.001). On the other hand, no significant difference is noted between experienced radiologists and SVM for pre-collapse detection. ANFIS algorithm for AVNFH detection with an AUC of 86.60% showed significantly higher performance than less experienced radiologists (AUC\u2009=\u200979.68%, p-value\u2009=\u20090.04). Although reaching less performance compared to experienced radiologists statistically not significant (AUC\u2009=\u200988.40%, p-value\u2009=\u20090.20).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our study has shed light on the remarkable capabilities of SVM and ANFIS as diagnostic tools for AVNFH detection in radiography. Their ability to achieve high accuracy with remarkable efficiency makes them promising candidates for early detection and intervention, ultimately contributing to improved patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12891-024-07669-7","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T23:41:24Z","timestamp":1721086884000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages"],"prefix":"10.1186","volume":"25","author":[{"given":"Nima","family":"Rakhshankhah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Abbaszadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atefeh","family":"Kazemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soroush Soltan","family":"Rezaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeid","family":"Roozpeykar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masoud","family":"Arabfard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"issue":"22","key":"7669_CR1","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.1056\/NEJM199205283262206","volume":"326","author":"HJ Mankin","year":"1992","unstructured":"Mankin HJ. 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