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  1. Book ; Conference proceedings: Artificial intelligence in medicine

    Quaglini, Silvana

    proceedings

    (Lecture notes in computer science ; 2101 : Lecture notes in artificial intelligence)

    2001  

    Event/congress Conference on Artificial Intelligence in Medicine in Europe (8, 2001, Cascais)
    Author's details 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001, Cascais, Portugal, July 1 - 4, 2001. Silvana Quaglini ... (ed.)
    Series title Lecture notes in computer science ; 2101 : Lecture notes in artificial intelligence
    Collection
    Keywords Medizin ; Künstliche Intelligenz
    Subject Artificial intelligence ; Computerunterstützte Intelligenz ; Maschinelle Intelligenz ; KI ; Humanmedizin ; Heilkunst ; Medicine
    Language English
    Size XIV, 469 S. : Ill., graph. Darst.
    Publisher Springer
    Publishing place Berlin u.a.
    Publishing country Germany
    Document type Book ; Conference proceedings
    HBZ-ID HT013079714
    ISBN 3-540-42294-3 ; 978-3-540-42294-5
    Database Catalogue ZB MED Medicine, Health

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  2. Article ; Online: Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities.

    Pala, Daniele / Petrini, Giorgia / Bosoni, Pietro / Larizza, Cristiana / Quaglini, Silvana / Lanzola, Giordano

    International journal of medical informatics

    2024  Volume 184, Page(s) 105351

    Abstract: Introduction: A proper nutrition is essential for human life. Recently, special attention on this topic has been given in relation to three health statuses: obesity, malnutrition and specific diseases that can be related to food or treated with specific ...

    Abstract Introduction: A proper nutrition is essential for human life. Recently, special attention on this topic has been given in relation to three health statuses: obesity, malnutrition and specific diseases that can be related to food or treated with specific diets. Mobile technology is often used to assist users that wish to regulate their eating habits, and identifying which fields of application have been explored the most by the app developers and which main functionalities have been adopted can be useful in view of future app developments.
    Methods: We selected 322 articles mentioning nutrition support apps through a literature database search, all of which have undergone an initial screening. After the exclusion of papers that were already reviews, not presenting apps or not focused on nutrition, not relevant or not developed for human subjects, 100 papers were selected for subsequent analyses that aimed at identifying the main treated conditions, outcome measures and functionalities implemented in the Apps.
    Results: Of the selected studies, 33 focus on specific diseases, 24 on obesity, 2 on malnutrition and 41 on other targets (e.g., weight/diet control). Type 2 diabetes is the most targeted disease, followed by gestational diabetes, hypertension, colorectal cancer and CVDs which all were targeted by more than one app. Most Apps include self-monitoring and coaching functionalities, educational content and artificial intelligence (AI) tools are slightly less common, whereas counseling, gamification and questionnaires are the least implemented. Body weight and calories/nutrients were the most common general outcome measures, while glycated hemoglobin (HbA1c) was the most common clinical outcome. No statistically significant differences in the effectiveness of the different functionalities were found.
    Conclusion: The use of mobile technology to improve nutrition has been widely explored in the last years, especially for weight control and specific diseases like diabetes; however, other food-related conditions such as Irritable Bowel Diseases appear to be less targeted by newly developed smartphone apps and their related studies. All different kinds of functionalities appear to be equally effective, but further specific studies are needed to confirm the results.
    MeSH term(s) Humans ; Mobile Applications ; Smartphone ; Diabetes Mellitus, Type 2/therapy ; Artificial Intelligence ; Obesity/therapy ; Malnutrition
    Language English
    Publishing date 2024-01-28
    Publishing country Ireland
    Document type Systematic Review ; Journal Article ; Review
    ZDB-ID 1466296-6
    ISSN 1872-8243 ; 1386-5056
    ISSN (online) 1872-8243
    ISSN 1386-5056
    DOI 10.1016/j.ijmedinf.2024.105351
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  3. Article ; Online: Azathioprine vs methotrexate in eosinophilic granulomatosis with polyangiitis: a monocentric retrospective study.

    Milanesi, Alessandra / Delvino, Paolo / Quaglini, Silvana / Montecucco, Carlomaurizio / Monti, Sara

    Rheumatology (Oxford, England)

    2023  Volume 63, Issue 4, Page(s) 945–952

    Abstract: Objectives: To analyse the effectiveness, safety and steroid-sparing effect of AZA and MTX as induction of remission and maintenance treatment in eosinophilic granulomatosis with polyangiitis.: Methods: We retrospectively collected data from 57 ... ...

    Abstract Objectives: To analyse the effectiveness, safety and steroid-sparing effect of AZA and MTX as induction of remission and maintenance treatment in eosinophilic granulomatosis with polyangiitis.
    Methods: We retrospectively collected data from 57 patients divided into four groups according to treatment: MTX/AZA as first-line agents (MTX1/AZA1) in non-severe disease or as second-line maintenance therapy (MTX2/AZA2) in severe disease previously treated with CYC/rituximab. During the first 5 years of treatment with AZA/MTX we compared the groups according to: remission rate [defined as R1: BVAS = 0; R2: BVAS = 0 with prednisone ≤5 mg/day; R3 (MIRRA definition): BVAS = 0 with prednisone ≤3.75 mg/day], persistence on therapy, cumulative glucocorticoid (GC) dose, relapse and adverse events (AEs).
    Results: There were no significant differences in remission rates (R1) in each group (63% in MTX1 vs 75% in AZA1, P = 0.53; 91% in MTX2 vs 71% in AZA2, P = 0.23). MTX1 allowed R2 more frequently in the first 6 months compared with AZA1 (54% vs 12%, P = 0.04); no patients receiving AZA1 achieved R3 up to the first 18 months (vs 35% in MTX1, P = 0.07). The cumulative GC dose was lower for MTX2 vs AZA2 (6 g vs 10.7 g at 5 years, P = 0.03). MTX caused more AEs compared with AZA (66% vs 30%, P = 0.004), without affecting the suspension rate. No differences emerged in time-to-first relapse, although fewer patients treated with AZA2 had asthma/ENT relapses (23% vs 64%, P = 0.04).
    Conclusion: A significant proportion of patients achieved remission with both MTX and AZA. MTX1 had an earlier remission on a lower GC dose but MTX2 had a better steroid-sparing effect.
    MeSH term(s) Humans ; Azathioprine/therapeutic use ; Methotrexate/therapeutic use ; Immunosuppressive Agents/therapeutic use ; Retrospective Studies ; Churg-Strauss Syndrome/drug therapy ; Prednisone/therapeutic use ; Granulomatosis with Polyangiitis/drug therapy ; Treatment Outcome ; Remission Induction ; Glucocorticoids/therapeutic use ; Recurrence
    Chemical Substances Azathioprine (MRK240IY2L) ; Methotrexate (YL5FZ2Y5U1) ; Immunosuppressive Agents ; Prednisone (VB0R961HZT) ; Glucocorticoids
    Language English
    Publishing date 2023-06-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 1464822-2
    ISSN 1462-0332 ; 1462-0324
    ISSN (online) 1462-0332
    ISSN 1462-0324
    DOI 10.1093/rheumatology/kead302
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  4. Article: Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma.

    Barkan, Ella / Porta, Camillo / Rabinovici-Cohen, Simona / Tibollo, Valentina / Quaglini, Silvana / Rizzo, Mimma

    Frontiers in oncology

    2023  Volume 13, Page(s) 1021684

    Abstract: Background and objectives: Investigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging ... ...

    Abstract Background and objectives: Investigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging Artificial Intelligence (AI) technologies to predict three- and five-year overall survival (OS) for mRCC patients starting their first-line of systemic treatment.
    Patients and methods: The retrospective study included 322 Italian patients with mRCC who underwent systemic treatment between 2004 and 2019. Statistical analysis included the univariate and multivariate Cox proportional-hazard model and the Kaplan-Meier analysis for the prognostic factors' investigation. The patients were split into a training cohort to establish the predictive models and a hold-out cohort to validate the results. The models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We assessed the clinical benefit of the models using decision curve analysis (DCA). Then, the proposed AI models were compared with well-known pre-existing prognostic systems.
    Results: The median age of patients in the study was 56.7 years at RCC diagnosis and 78% of participants were male. The median survival time from the start of systemic treatment was 29.2 months; 95% of the patients died during the follow-up that finished by the end of 2019. The proposed predictive model, which was constructed as an ensemble of three individual predictive models, outperformed all well-known prognostic models to which it was compared. It also demonstrated better usability in supporting clinical decisions for 3- and 5-year OS. The model achieved (0.786 and 0.771) AUC and (0.675 and 0.558) specificity at sensitivity 0.90 for 3 and 5 years, respectively. We also applied explainability methods to identify the important clinical features that were found to be partially matched with the prognostic factors identified in the Kaplan-Meier and Cox analyses.
    Conclusions: Our AI models provide best predictive accuracy and clinical net benefits over well-known prognostic models. As a result, they can potentially be used in clinical practice for providing better management for mRCC patients starting their first-line of systemic treatment. Larger studies would be needed to validate the developed model.
    Language English
    Publishing date 2023-02-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2649216-7
    ISSN 2234-943X
    ISSN 2234-943X
    DOI 10.3389/fonc.2023.1021684
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  5. Article ; Online: V-care: An Application to Support Lifestyle Improvement in Children with Obesity.

    Larizza, Cristiana / Quaglini, Silvana / Chasseur, Michelangelo / Bevolo, Valentina / Zuccotti, Gianvincenzo / Calcaterra, Valeria

    Studies in health technology and informatics

    2023  Volume 302, Page(s) 957–961

    Abstract: Obesity is increasing in the pediatric population and it represents an important risk factor for the life-long development of several diseases. The aim of this work is to reduce children obesity through an educational program delivered through a mobile ... ...

    Abstract Obesity is increasing in the pediatric population and it represents an important risk factor for the life-long development of several diseases. The aim of this work is to reduce children obesity through an educational program delivered through a mobile application. Novelties of our approach are the involvement of the families in the program and a design inspired to psychological/behavioral change theories, with the aim of maximizing the chance of patients' compliance to the program. A pilot usability and acceptability study has been performed on ten children aged 6-12 years using a questionnaire to evaluate eight system features on a Likert scale from 1 to 5. Encouraging results were obtained: mean scores were all above 3.
    MeSH term(s) Humans ; Child ; Life Style ; Pediatric Obesity/prevention & control ; Patient Compliance ; Risk Factors
    Language English
    Publishing date 2023-05-19
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
    DOI 10.3233/SHTI230317
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  6. Article ; Online: DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms.

    Gerbasi, Alessia / Clementi, Greta / Corsi, Fabio / Albasini, Sara / Malovini, Alberto / Quaglini, Silvana / Bellazzi, Riccardo

    Computer methods and programs in biomedicine

    2023  Volume 235, Page(s) 107483

    Abstract: Background and objective: Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as ... ...

    Abstract Background and objective: Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases.
    Methods: DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs.
    Results: The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results.
    Conclusion: To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.
    MeSH term(s) Humans ; Female ; Mammography/methods ; Breast Diseases/diagnostic imaging ; Breast Diseases/pathology ; Breast Neoplasms/diagnostic imaging ; Algorithms ; Calcinosis/diagnostic imaging
    Language English
    Publishing date 2023-03-31
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 632564-6
    ISSN 1872-7565 ; 0169-2607
    ISSN (online) 1872-7565
    ISSN 0169-2607
    DOI 10.1016/j.cmpb.2023.107483
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  7. Article ; Online: V-care: An application to support lifestyle improvement in children with obesity.

    Larizza, Cristiana / Bosoni, Pietro / Quaglini, Silvana / Chasseur, Michelangelo / Bevolo, Valentina / Zuccotti, Gianvincenzo / Calcaterra, Valeria

    International journal of medical informatics

    2023  Volume 177, Page(s) 105140

    Abstract: Background: Obesity is increasing in the pediatric population, and it represents an important risk factor for the life-long development of several diseases. Although health promotion represents the mainstay of obesity prevention and treatment, lifestyle ...

    Abstract Background: Obesity is increasing in the pediatric population, and it represents an important risk factor for the life-long development of several diseases. Although health promotion represents the mainstay of obesity prevention and treatment, lifestyle modification programs are often unsuccessful.
    Objectives: The purpose of this article is to introduce the V-care app, a mobile health platform specifically developed to offer effective interaction and support young people in a long-term obesity treatment, combining different strategies to maximize the results of the lifestyle modification program and minimize the possibility of dropouts.
    Methods: The V-care app is based on a conventional client-server architecture, but novelties of our approach are the involvement of families in the lifestyle modification program, and the design inspired to psychological/behavioral change theories, with the aim of raising the chance of patients' compliance to the program. V-care implements a goal-based behavioral intervention, providing specific feedbacks according to the patient's performance. A pilot usability and acceptability study was performed on a sample of thirteen children aged 6-12 years, using a questionnaire with a 5-points Likert scale to evaluate eight system features, identified as essential requirements based on the analysis of strengths and weaknesses of similar systems in literature.
    Results: The pilot study highlighted very high rate of overall friendliness and perceived utility evaluation, while some critical issues emerged especially for the chatbot section, which may be due to the novelty of the technology. The positive evaluation of the design choices is confirmed by the average score greater than 3 for all the questions.
    Conclusions: The V-care app represents a digital innovation in the pediatric healthcare, and it could be introduced in children's primary healthcare nationwide, with the aim to offer an intervention program for controlling and preventing childhood obesity.
    MeSH term(s) Humans ; Child ; Adolescent ; Pediatric Obesity/prevention & control ; Pilot Projects ; Life Style ; Health Promotion/methods ; Patient Compliance
    Language English
    Publishing date 2023-07-08
    Publishing country Ireland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1466296-6
    ISSN 1872-8243 ; 1386-5056
    ISSN (online) 1872-8243
    ISSN 1386-5056
    DOI 10.1016/j.ijmedinf.2023.105140
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  8. Article ; Online: Obstructive Sleep Apnea Home-Monitoring Using a Commercial Wearable Device.

    Mokhtaran, Mehrshad / Sacchi, Lucia / Tibollo, Valentina / Risi, Irene / Ramella, Vittoria / Quaglini, Silvana / Fanfulla, Francesco

    Studies in health technology and informatics

    2022  Volume 290, Page(s) 522–525

    Abstract: Obstructive sleep apnea (OSA) is a common sleep disorder and polysomnography (PSG) is the gold standard for its diagnosis and treatment monitoring. There are nowadays several activity trackers measuring sleep quality through the detection of sleep stages. ...

    Abstract Obstructive sleep apnea (OSA) is a common sleep disorder and polysomnography (PSG) is the gold standard for its diagnosis and treatment monitoring. There are nowadays several activity trackers measuring sleep quality through the detection of sleep stages. To allow an easier monitoring of the treatment efficacy at home, this work explores the possibility of using one of those commercial smart-bands. To this aim, we studied the signals provided by PSG and a Fitbit smart-band on 26 consecutive patients, admitted to the hospital after the diagnosis of OSA, and submitted to ventilation or positional treatment. They underwent monitoring for three nights (basal, titration, and control). We developed both a visualization software allowing doctors to visually compare the two hypnograms, and a set of statistics for assessing the concordance of the two methods. Results indicate that Fitbit can detect normal sleep patterns, while it is less able to detect the abnormal ones.
    MeSH term(s) Fitness Trackers ; Humans ; Polysomnography/methods ; Sleep Apnea, Obstructive/diagnosis ; Sleep Apnea, Obstructive/therapy ; Sleep Stages ; Wearable Electronic Devices
    Language English
    Publishing date 2022-06-08
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
    DOI 10.3233/SHTI220131
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  9. Article: Corrigendum: Diagnostic Delay of Pulmonary Embolism in COVID-19 Patients.

    Melazzini, Federica / Reduzzi, Margherita / Quaglini, Silvana / Fumoso, Federica / Lenti, Marco Vincenzo / Di Sabatino, Antonio

    Frontiers in medicine

    2022  Volume 9, Page(s) 884680

    Abstract: This corrects the article DOI: 10.3389/fmed.2021.637375.]. ...

    Abstract [This corrects the article DOI: 10.3389/fmed.2021.637375.].
    Language English
    Publishing date 2022-03-23
    Publishing country Switzerland
    Document type Published Erratum
    ZDB-ID 2775999-4
    ISSN 2296-858X
    ISSN 2296-858X
    DOI 10.3389/fmed.2022.884680
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  10. Article ; Online: Eliciting and Exploiting Utility Coefficients in an Integrated Environment for Shared Decision-Making.

    Salvi, Elisa / Parimbelli, Enea / Quaglini, Silvana / Sacchi, Lucia

    Methods of information in medicine

    2019  Volume 58, Issue 1, Page(s) 24–30

    Abstract: Background: In shared decision-making, a key step is quantifying the patient's preferences in relation to all the possible outcomes of the compared clinical options. According to utility theory, this can be done by eliciting utility coefficients (UCs) ... ...

    Abstract Background: In shared decision-making, a key step is quantifying the patient's preferences in relation to all the possible outcomes of the compared clinical options. According to utility theory, this can be done by eliciting utility coefficients (UCs) from the patient. The obtained UCs are then used in decision models (e.g., decision trees). The elicitation process involves the choice of one or more elicitation methods, which is not easy for decision-makers who are unfamiliar with the theoretical framework. Moreover, to our knowledge there are no tools that integrate functionalities for UC elicitation with functionalities to run decision models that include the elicited values.
    Objectives: The first aim of this work is to provide decision support to the clinicians for the selection of the elicitation method. The second aim is to bridge the gap between UC elicitation and the exploitation of those UCs in shared decision-making.
    Methods: Based on evidence from the utility theory literature, we developed a set of production rules that recommend the optimal elicitation method(s) according to the patient's profile and health state. We then complemented this decision support tool with a functionality for quantifying and running decision trees defined through the commercial software TreeAge.
    Results: The result is an integrated framework for shared decision-making. Given the primary aim of this work, we focus for result evaluation on the elicitation tool. It was tested on 51 volunteers, who expressed UCs for four purposely selected health states. The insights on the collected UCs validated the rules included in the decision support system. The usability of the tool was assessed through the System Usability Scale, obtaining positive results.
    Conclusion: We developed an integrated environment to facilitate shared decision-making in the clinical practice. The next step is the validation of the entire framework and its use besides shared decision-making. As a matter of fact, it may also be exploited to target cost-utility analysis to a specific patient population.
    MeSH term(s) Decision Making ; Decision Support Techniques ; Decision Trees ; Humans
    Language English
    Publishing date 2019-07-05
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 3500-2
    ISSN 2511-705X ; 0026-1270
    ISSN (online) 2511-705X
    ISSN 0026-1270
    DOI 10.1055/s-0039-1692416
    Database MEDical Literature Analysis and Retrieval System OnLINE

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