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Famiglini L, Campagner A, Barandas M, La Maida GA, Gallazzi E, Cabitza F. Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems. Comput Biol Med 2024; 170:108042. [PMID: 38308866 DOI: 10.1016/j.compbiomed.2024.108042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/19/2023] [Accepted: 01/26/2024] [Indexed: 02/05/2024]
Abstract
This paper proposes a user study aimed at evaluating the impact of Class Activation Maps (CAMs) as an eXplainable AI (XAI) method in a radiological diagnostic task, the detection of thoracolumbar (TL) fractures from vertebral X-rays. In particular, we focus on two oft-neglected features of CAMs, that is granularity and coloring, in terms of what features, lower-level vs higher-level, should the maps highlight and adopting which coloring scheme, to bring better impact to the decision-making process, both in terms of diagnostic accuracy (that is effectiveness) and of user-centered dimensions, such as perceived confidence and utility (that is satisfaction), depending on case complexity, AI accuracy, and user expertise. Our findings show that lower-level features CAMs, which highlight more focused anatomical landmarks, are associated with higher diagnostic accuracy than higher-level features CAMs, particularly among experienced physicians. Moreover, despite the intuitive appeal of semantic CAMs, traditionally colored CAMs consistently yielded higher diagnostic accuracy across all groups. Our results challenge some prevalent assumptions in the XAI field and emphasize the importance of adopting an evidence-based and human-centered approach to design and evaluate AI- and XAI-assisted diagnostic tools. To this aim, the paper also proposes a hierarchy of evidence framework to help designers and practitioners choose the XAI solutions that optimize performance and satisfaction on the basis of the strongest evidence available or to focus on the gaps in the literature that need to be filled to move from opinionated and eminence-based research to one more based on empirical evidence and end-user work and preferences.
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Affiliation(s)
- Lorenzo Famiglini
- Department of Computer Science, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
| | | | - Marilia Barandas
- Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, Porto, Portugal
| | | | - Enrico Gallazzi
- Istituto Ortopedico Gaetano Pini - ASST Pini-CTO, Milan, Italy
| | - Federico Cabitza
- Department of Computer Science, Systems and Communication, University of Milano-Bicocca, Milan, Italy; IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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Lombardi A, Arezzo F, Di Sciascio E, Ardito C, Mongelli M, Di Lillo N, Fascilla FD, Silvestris E, Kardhashi A, Putino C, Cazzolla A, Loizzi V, Cazzato G, Cormio G, Di Noia T. A human-interpretable machine learning pipeline based on ultrasound to support leiomyosarcoma diagnosis. Artif Intell Med 2023; 146:102697. [PMID: 38042596 DOI: 10.1016/j.artmed.2023.102697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 10/08/2023] [Accepted: 10/29/2023] [Indexed: 12/04/2023]
Abstract
The preoperative evaluation of myometrial tumors is essential to avoid delayed treatment and to establish the appropriate surgical approach. Specifically, the differential diagnosis of leiomyosarcoma (LMS) is particularly challenging due to the overlapping of clinical, laboratory and ultrasound features between fibroids and LMS. In this work, we present a human-interpretable machine learning (ML) pipeline to support the preoperative differential diagnosis of LMS from leiomyomas, based on both clinical data and gynecological ultrasound assessment of 68 patients (8 with LMS diagnosis). The pipeline provides the following novel contributions: (i) end-users have been involved both in the definition of the ML tasks and in the evaluation of the overall approach; (ii) clinical specialists get a full understanding of both the decision-making mechanisms of the ML algorithms and the impact of the features on each automatic decision. Moreover, the proposed pipeline addresses some of the problems concerning both the imbalance of the two classes by analyzing and selecting the best combination of the synthetic oversampling strategy of the minority class and the classification algorithm among different choices, and the explainability of the features at global and local levels. The results show very high performance of the best strategy (AUC = 0.99, F1 = 0.87) and the strong and stable impact of two ultrasound-based features (i.e., tumor borders and consistency of the lesions). Furthermore, the SHAP algorithm was exploited to quantify the impact of the features at the local level and a specific module was developed to provide a template-based natural language (NL) translation of the explanations for enhancing their interpretability and fostering the use of ML in the clinical setting.
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Affiliation(s)
- Angela Lombardi
- Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy.
| | - Francesca Arezzo
- Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Eugenio Di Sciascio
- Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy
| | - Carmelo Ardito
- Department of Engineering, LUM "Giuseppe Degennaro" University, Casamassima, Bari, Italy
| | - Michele Mongelli
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari "Aldo Moro", Bari, Italy
| | - Nicola Di Lillo
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari "Aldo Moro", Bari, Italy
| | | | - Erica Silvestris
- Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Anila Kardhashi
- Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Carmela Putino
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari "Aldo Moro", Bari, Italy
| | - Ambrogio Cazzolla
- Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Vera Loizzi
- Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy; Interdisciplinar Department of Medicine, University of Bari "Aldo Moro", Bari, Italy
| | - Gerardo Cazzato
- Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari "Aldo Moro", Bari, Italy
| | - Gennaro Cormio
- Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy; Interdisciplinar Department of Medicine, University of Bari "Aldo Moro", Bari, Italy
| | - Tommaso Di Noia
- Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy
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