Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning
The detection and classification of cystic lesions of the jaw is of high clinical relevance and represents a topic of interest in medical artificial intelligence research. The human clinical diagnostic reasoning process uses contextual information, including the spatial relation of the detected lesi...
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2022
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| Online Access: | https://hdl.handle.net/1721.1/144430 |
| _version_ | 1826193126814908416 |
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| author | Feher, Balazs Kuchler, Ulrike Schwendicke, Falk Schneider, Lisa Cejudo Grano de Oro, Jose Eduardo Xi, Tong Vinayahalingam, Shankeeth Hsu, Tzu-Ming Harry Brinz, Janet Chaurasia, Akhilanand Dhingra, Kunaal Gaudin, Robert Andre Mohammad-Rahimi, Hossein Pereira, Nielsen Perez-Pastor, Francesc Tryfonos, Olga Uribe, Sergio E. Hanisch, Marcel Krois, Joachim |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Feher, Balazs Kuchler, Ulrike Schwendicke, Falk Schneider, Lisa Cejudo Grano de Oro, Jose Eduardo Xi, Tong Vinayahalingam, Shankeeth Hsu, Tzu-Ming Harry Brinz, Janet Chaurasia, Akhilanand Dhingra, Kunaal Gaudin, Robert Andre Mohammad-Rahimi, Hossein Pereira, Nielsen Perez-Pastor, Francesc Tryfonos, Olga Uribe, Sergio E. Hanisch, Marcel Krois, Joachim |
| author_sort | Feher, Balazs |
| collection | MIT |
| description | The detection and classification of cystic lesions of the jaw is of high clinical relevance and represents a topic of interest in medical artificial intelligence research. The human clinical diagnostic reasoning process uses contextual information, including the spatial relation of the detected lesion to other anatomical structures, to establish a preliminary classification. Here, we aimed to emulate clinical diagnostic reasoning step by step by using a combined object detection and image segmentation approach on panoramic radiographs (OPGs). We used a multicenter training dataset of 855 OPGs (all positives) and an evaluation set of 384 OPGs (240 negatives). We further compared our models to an international human control group of ten dental professionals from seven countries. The object detection model achieved an average precision of 0.42 (intersection over union (IoU): 0.50, maximal detections: 100) and an average recall of 0.394 (IoU: 0.50–0.95, maximal detections: 100). The classification model achieved a sensitivity of 0.84 for odontogenic cysts and 0.56 for non-odontogenic cysts as well as a specificity of 0.59 for odontogenic cysts and 0.84 for non-odontogenic cysts (IoU: 0.30). The human control group achieved a sensitivity of 0.70 for odontogenic cysts, 0.44 for non-odontogenic cysts, and 0.56 for OPGs without cysts as well as a specificity of 0.62 for odontogenic cysts, 0.95 for non-odontogenic cysts, and 0.76 for OPGs without cysts. Taken together, our results show that a combined object detection and image segmentation approach is feasible in emulating the human clinical diagnostic reasoning process in classifying cystic lesions of the jaw. |
| first_indexed | 2024-09-23T09:34:08Z |
| format | Article |
| id | mit-1721.1/144430 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:34:08Z |
| publishDate | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1444302023-04-20T14:48:10Z Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning Feher, Balazs Kuchler, Ulrike Schwendicke, Falk Schneider, Lisa Cejudo Grano de Oro, Jose Eduardo Xi, Tong Vinayahalingam, Shankeeth Hsu, Tzu-Ming Harry Brinz, Janet Chaurasia, Akhilanand Dhingra, Kunaal Gaudin, Robert Andre Mohammad-Rahimi, Hossein Pereira, Nielsen Perez-Pastor, Francesc Tryfonos, Olga Uribe, Sergio E. Hanisch, Marcel Krois, Joachim Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory The detection and classification of cystic lesions of the jaw is of high clinical relevance and represents a topic of interest in medical artificial intelligence research. The human clinical diagnostic reasoning process uses contextual information, including the spatial relation of the detected lesion to other anatomical structures, to establish a preliminary classification. Here, we aimed to emulate clinical diagnostic reasoning step by step by using a combined object detection and image segmentation approach on panoramic radiographs (OPGs). We used a multicenter training dataset of 855 OPGs (all positives) and an evaluation set of 384 OPGs (240 negatives). We further compared our models to an international human control group of ten dental professionals from seven countries. The object detection model achieved an average precision of 0.42 (intersection over union (IoU): 0.50, maximal detections: 100) and an average recall of 0.394 (IoU: 0.50–0.95, maximal detections: 100). The classification model achieved a sensitivity of 0.84 for odontogenic cysts and 0.56 for non-odontogenic cysts as well as a specificity of 0.59 for odontogenic cysts and 0.84 for non-odontogenic cysts (IoU: 0.30). The human control group achieved a sensitivity of 0.70 for odontogenic cysts, 0.44 for non-odontogenic cysts, and 0.56 for OPGs without cysts as well as a specificity of 0.62 for odontogenic cysts, 0.95 for non-odontogenic cysts, and 0.76 for OPGs without cysts. Taken together, our results show that a combined object detection and image segmentation approach is feasible in emulating the human clinical diagnostic reasoning process in classifying cystic lesions of the jaw. 2022-08-25T12:42:21Z 2022-08-25T12:42:21Z 2022-08-14 2022-08-25T11:17:44Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/144430 Diagnostics 12 (8): 1968 (2022) PUBLISHER_CC http://dx.doi.org/10.3390/diagnostics12081968 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Feher, Balazs Kuchler, Ulrike Schwendicke, Falk Schneider, Lisa Cejudo Grano de Oro, Jose Eduardo Xi, Tong Vinayahalingam, Shankeeth Hsu, Tzu-Ming Harry Brinz, Janet Chaurasia, Akhilanand Dhingra, Kunaal Gaudin, Robert Andre Mohammad-Rahimi, Hossein Pereira, Nielsen Perez-Pastor, Francesc Tryfonos, Olga Uribe, Sergio E. Hanisch, Marcel Krois, Joachim Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning |
| title | Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning |
| title_full | Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning |
| title_fullStr | Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning |
| title_full_unstemmed | Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning |
| title_short | Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning |
| title_sort | emulating clinical diagnostic reasoning for jaw cysts with machine learning |
| url | https://hdl.handle.net/1721.1/144430 |
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