Problematic Video Game Use among Teenagers in Sfax, Tunisia
Received Date: Jul 07, 2018 / Accepted Date: Jul 20, 2018 / Published Date: Jul 28, 2018
Background: The aim of this study was to identify the prevalence and risk factors for Problematic video game use among Tunisian adolescents.
Methods: A cross-sectional study was carried out on 578 secondary school students, aged 14 to 20 years. They were recruited from seven secondary schools then randomly selected from the urban area of Sfax. The self-administered Fisher’s 9-item questionnaire was used in this survey. To identify an associated problematic internet use, Young’s 8-item questionnaire was used. The Hospital Anxiety and Depression scale (HAD) was administered to screen for anxiety and depression symptoms. An anonymous self-administered questionnaire covered socio-demographic, individual and family data.
Results: The prevalence of problematic video game use among urban Tunisian high school students was 14.01%. Risk factors were anxiety symptoms (p=0.034, ORA=2.09), poor relationships within the family (p=0.001, ORA=2.62), mothers’ employment as mid to high level executives (p=0.002, ORA=2.72), no parental limitations on the amount of time spent on playing video games (p=0.000, ORA=3.44), an associated problematic internet use (p=0.000, ORA=3.47) and no playing sports (p=0.011, ORA=3.67).
Discussion: The observed rate of the affected adolescents point out to a need for effective education and prevention programs. The identification of risk factors can help to alert mental health providers to be careful to screen these patients for problematic video game use.
Keywords: Addiction; Epidemiology; Adolescent psychiatry; Risk factor
Playing video games is now a major leisurely pursuit among adolescents in many parts of the world [1-3]. Initially, playing is not pathological but it becomes so for some individuals when the activity becomes dysfunctional. Internet gaming disorder has been included in the emerging measures and models section of the fifth edition of the Diagnostic Statistical Manual [4,5] as a subject of further empirical enquiry. The terms used to describe problematic video game use (PVU) vary across the research literature . Data from various countries around the world suggest that between 0.2% to 15.5% of the adolescents are engaged in PVU [6-10]. A summary of prevalence studies found that there was a higher prevalence of problematic video gaming in East Asian populations, compared to Western European, North American and Australian populations .
The study of risk factors for PVU has the potential to help develop interventions to combat this condition. Researchers have been investigating whether there is an association between computer gaming and smoking, drug use, depression , social anxieties , hyperactivity , and sensation seeking . Most of these studies found correlations between problem gaming and negative outcomes.
In Tunisia, like in many parts of the world, playing online or offline games has become a popular activity among adolescents. Tunisia, which is a relatively small and homogeneous country, belongs to the Maghreb region of North Africa and the Arab world. It is known for its religious and ethnic homogeneity as well as its deeply rooted historical commitment to moderate Islam. According to the 2016 statistics, its population was estimated at about 12 million people. Its economy has historically depended on olive oil, phosphates, agri-food products, car parts manufacturing, and tourism. Sfax is a city in Tunisia of about 955,500 inhabitants. The city of Sfax occupies a pivotal place on the national scene and is often described as Tunisia’s “second city” after the capital Tunis. These socioeconomic and culture factors have made high-tech devices (computer, tablet, and smartphone) become a part of everyday life in Sfax, In fact, the rapid increase of Internet cafes and PC bangs in front of the lack or insufficient reinforcement of video game copyright protections and illegal selling of video game CD at cheap prices lead us to think about the prevalence and risk factors of PVU in Sfax, which could be as high as what is registered in East Asian countries. Previous studies have focused on the prevalence and risk factors of problematic Internet use among teenagers in Tunisia [16,17]. However, to the best of our knowledge, no study has so far focused on the prevalence and risk factors of PVU among adolescents in Tunisia.
Materials and Methods
This study has extended over a month from January 15 to February 15, 2009 and covered students enrolled from all the public schools in communal areas of the city of Sfax. The subjects were selected by random sampling in two stages. The first stage involved cluster sampling, which selected schools from a list of the all upper secondary schools in the communal areas of Sfax. In the second stage, students were stratified by grade level. The basis of this survey consists of a list of classes provided by the headmasters of the schools that were selected in the first stage of sampling. The minimum size of the sample was 600 students based upon the prevalence rates of PVU reported in several previous studies, which vary between 0.2% and 15.5% [6-10]. To constitute this sample, seven secondary schools amongst the twenty one public secondary schools in Sfax were drawn. Then, for each selected secondary school, three classes were randomly selected. The sample was eventually composed of 578 students, 263 (45.5%) males and 315 (54.49%) females. The age of these students ranged between 14 and 20 years, with an average age of 16 years (SD=1.26).
All the students in the selected classes were informed about the objectives of the study and the procedures to participate. The number of responses was 578 out of 600 which corresponds to a rate of 96.33%. Questionnaires were distributed by the teachers during the classes and were completed anonymously in the classrooms during class time. The student’s name or other identifying information was not included, except for the student’s grade, age and school. The research was approved by the Ethics Committee of the Medicine University of Sfax.
All the screening instruments were translated into Arabic. Screening for PVU was performed using the questionnaire developed by Fischer . which was thought to have adequate validity and reliability. This questionnaire was also based on the DSM-IV criteria for gambling addiction . Problematic video game use is detected if the subject meets four of nine criteria.
We also assessed Internet addiction using Young Internet Addiction Test (IAT) . one of the most widely used measures of internet addiction. This eight-item questionnaire was adapted from the DSM-IV criteria for pathological gambling . Each question was answered yes or no. Five or more “yes” responses were considered to be a diagnostic of internet addiction. The Arabic version of this questionnaire has been recently validated on an adolescent sample . In order to assess psychiatric symptoms in our study sample, we used the Hospital Anxiety and Depression (HAD) Scale , a widely used self-rating scale that scans anxiety and depression symptoms. The cutoff point for the HAD depression and anxiety subscales is ≥ 10.
In addition to demographic characteristics, survey instruments included several items that were specifically designed to capture the amount of time adolescents spend playing video games. Compulsive tobacco and alcohol consumption were assessed on the basis of two questions: “Do you consider yourself incapable of quitting smoking/ drinking?” Data concerning socio-demographic, family related, individual and environmental factors were gathered on a form completed by respondents. Subjects were asked to classify each parent’s occupation according to the following four categories: 1) not working, 2) laborer, 3) middle level manager, or 4) senior level manager. Family size was assessed by asking for the number of family members living in the home. A family having more than three children was defined as large. The following questions had yes or no answers: “Do you play sports regularly,” “Do you have friends?” and “Are you involved in an artistic activity, such as music, dance, drawing or theater?” Familial relationship quality was assessed with the question “How would you describe your communication with your family members?” Participants could choose one of four responses: nonexistent, poor, average, good.
Data entry was performed using the epidemiology and statistics software of SPSS and Excel. Descriptive statistics were used to identify the frequencies for qualitative variables and averages for quantitative variables. A Chi Square analysis was applied to look for factors related to PVU. Rejection of the null hypothesis was set at P<0.05. A multivariate logistic regression analysis was used to adjust potential confounding factors.
Prevalence of problematic video game use
Table 1 shows the number and percentage of students meeting criteria for PVU.
|Males (%)||Females (%)||Total (%)|
* Problematic Video game Use
Table 1: Physicochemical properties of complex.
Risk factors of problematic video game use
Socio-demographic risk factors: Table 2 shows socio-demographic risk factors for PVU. Neither sex nor father’s occupation was predictive, but mother’s being employed as a middle to upper-level executive was significantly correlated with PVU, as shown in Table 2.
|Mid- to upper level manager executive||56.91||45.94|
|Mid- to upper level manager executive||37.81||16.04|
* Problematic Video game Use
Table 2: Socio-demographic risk factors for problematic video game use (PVU).
Individual risk factors: Individual characteristics associated with PVU are shown in Table 3. Problematic Internet use, depression and anxiety symptoms emerged as the three strongest individual risk factors for PVU. Engaging in sports activities had a protective effect.
|Factors||PVU * (%)||No PVU*(%)||p|
|Poor school performance||37.04||36.69||0.95|
|Problematic Internet use||43.09||13.86||0.000|
* Problematic Video game Use
Table 3: Individual risk factors for problematic video game use (PVU).
Family risk factors: Table 4 shows family variables. Poor family relationships and familial violence were correlated with PVU.
|Factors||PVU* (%)||No PVU* (%)||p|
|Family psychiatric history||4.87||13.86||0,32|
|Poor family relationships||53.1||41.9||0,059|
* Problematic Video game Use
Table 4: Family risk factors for problematic video game use (PVU).
Risk factors related to access to video games: Table 5 shows risk factors related to access to video games. PVU was significantly associated with spending five or more hours a day on the Internet (as reported by the adolescent). No parental control of the amount of time their adolescents spent on video game use, and poor dialogue about video games between parents and adolescents were significantly correlated with PVU.
|Factors||PVU* (%)||No PVU* (%)||p|
|Supervision of types of video game
used by their adolescents
|Supervision of time spent on
playing video games
|Poor dialogue about video games
between parents and adolescents
|Video game playing time
(mean ± SD hours per week)
* Problematic Video game Use
Table 5: Access and parental supervision in problematic video game use (PVU).
Multivariate analysis: Six variables were identified as incremental risk factors for PVU (Table 6). These risk factors consist of anxiety symptoms (p=0.034, ORA=2.09), poor relationships within the family (p=0.001, ORA=2.62), mothers’ employment as mid to high level executives (p=0.002, ORA=2.72), no parental limitations on the amount of time spent on playing video games (p=0.000, ORA=3.44), an associated problematic internet use (p=0.000, ORA=3.47) and no playing sports (p=0.011, ORA=3.67).
|Covariables||Univariate analysis||Multivariate analysis|
|Mother’s occupation mid- to upper level manager executive||3.09||0.000||2.72||1.45-5.1||0.002|
|Problematic Internet use||4.93||0.000||3.47||1.87-6.43||0.000|
|Poor family relationships||1.57||0.05||2.62||1.46-4.69||0.001|
|Poor dialogue about video games between parents and adolescents||2.06||0.01|
|No supervision of time spent on
playing video games
* Odd Ratio
** Adjusted Odds Ratio
***95% Confidence Interval
Table 6: Univariate and multivariate analysis.
In the present study, the prevalence of PVU among urban high school students in Sfax city is 14.01%. International estimates of PVU widely vary. In fact, in the USA, it is 8.0% in Australia [7,8] in Germany 11.9%,  and 15.6% in China . In the present study, the PVU prevalence rate indicates prevalence higher than the one found in other Asian countries [10,23]. It is unclear if the reported wide range of prevalence estimates is related to cultural differences between regions or countries, or because of the different approaches applies in the definition and evaluation of PVU. The absence of a standardized definition covering all ages and genres could cause this variety of prevalence. In addition, measurement instruments differ from one study to another. In fact, the lack of validation of a standard universal instrument based on previous studies is a critical problem. The prevalence of PVU recorded in the present study is among the highest figures in the literature. Several factors may explain this: on the one hand, other researchers distinguish between high engagement and problematic use . In this study, no clear boundaries between the two groups appear. The present study would also have been improved by collecting information from additional sources, like parents and teachers. A clinical examination would be necessary to support the diagnosis of PVU. On the one hand, the present study is not representative of the school population as it was carried out exclusively in urban areas where access to video games is easier than in the rural ones. On the other hand, great contrasts exist between the affluent, urban and educated segments of the population, and the rural, illiterate, and disadvantaged. Besides, access to health, education, potable water, electricity and employment is a real problem in the rural areas of Tunisia. In fact, video games in these regions are luxury.
To the best of our knowledge, this is the first published report on the risk factors of PVU among adolescents in Tunisia.
In the present study, the results are not in line with what was found in previous research studies which stated that males plays video games PVU more than females do [5,25]. Moreover, there is a strong relationship between PVU and the number of hours invested in video games as mentioned in other studies . The occupation of the mother as middle to upper-level executive was significantly associated as a risk factor for PVU. Tunisian women have been known for their high level of education compared to their counterparts in the Arab world. The global number of Tunisian graduate women reflects this. This finding may be explained by the fact that the increasing access to video games from home is among the higher income Tunisian families. In addition, it has been suggested that some parents support their teenagers’ choices to spend a great amount of their leisure time in front of the screen because they think that it is better to keep their children at home than let them go out, which may expose them to danger. In the present study, a self-reported lack of playing sports is associated with PVU. This result is controversial is the literature [26-28]. In the present study, it was found that 43.09% of the adolescents meeting Fisher’s criteria for PVU also meet Young’s criteria for Internet addiction which joins the results of Van Rooij . Moreover, anxiety and depression symptoms in our sample were significantly more prevalent among adolescents who were identified in the group with PVU than in the non PVU group. Domingues et al.  found that screen time has been negatively associated with depression and anxiety. It is possible that for anxious individuals, PVU will have an anxiolytic function like other kinds of addictive behaviors. It may also aggravate pre-existing anxiety disorders. It is unclear if anxiety increases the risk for an adolescent to become a pathological gamer, or a pathological gaming increases the risk of anxiety disorders . Moreover, in this study, the lack of supervision by parents of the time spent playing video games is considered as the second most important risk factor. This finding also joins the results of Muñoz et al.  and supports the opinion of social criticism which pointed to a trend towards an increasing isolation of the family members and unresolved and unspoken family conflicts as risk factors for PVU.
Our data are limited by the fact that we did not obtain corroborating data from other sources mainly from parents. Confirmation of the PVU diagnosis by means of a psychiatric interview is essential. Another limitation for this study is that it is not representative of the entire city of Sfax since it was conducted only in its urban area. Given the crosssectional nature of the study, causality cannot be inferred. This leads to the question as to whether PVU is a unique phenomenon or whether it reflects the co-occurring symptoms of underlying mental health impairments. Despite these limitations, we contend that our study makes several important contributions to the video gaming literature. In fact, no studies of PVU have been conducted at the national level. Therefore, the present study is the first to provide data to answer questions about the prevalence and risk factors of PVU among the youth in Tunisia. In conclusion, and given the vulnerability of adolescents who are facing developmental challenges, a good understanding of video gaming activities among this group would be helpful for tailoring effective education or prevention programs to promote their health. The observed rate of the affected adolescents reflects the need for effective education and prevention programs or strategies in Sfax to avoid negative effects of video gaming on adolescents. In this study of urban Tunisian adolescents, we found several socio-demographic, family related, individual and environmental factors to be associated with PVU. It seems unlikely that PVU could be predicted by one factor; therefore, it is probably necessary to have the co-occurrence of several factors. In fact, it is important to inform parents that moderate video game use may be a positive experience whereas an excessive use may cause problems. However, several important questions, including information on how children can be helped, and what type of help might be most effective, remain unanswered.
- Simmons AB, Chappell SG (1998)Artificial intelligence-definition and practice. IEEE Journal of Oceanic Engineering 13: 14-42.
- Minsky M (1961) Steps toward artificial intelligence. Proceedings of the IRE 49: 8-30.
- Kahn CE (1994) Artificial intelligence in radiology: decision support systems. RadioGraphics 14: 849-861.
- Miller RA (1994) Medical Diagnostic Decision Support Systems--Past, Present, And Future: A Threaded Bibliography and Brief Commentary. Journal of the American Medical Informatics Association 1: 8-27.
- Siegel E (2012) Artificial intelligence and diagnostic radiology: Not quite ready to welcome our computer overlords. Applied Radiology 41: 8.
- Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl PA, et al. (2013) Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine 11: 47-58.
- Krupinski EA (2003) The Future of Image Perception in Radiology. Academic Radiology 10: s1-3.
- Garland LH (1949) On the scientific evaluation of diagnostic procedures. Radiology 52: 309-328.
- Borgstede JP, Lewis RS, Bhargavan M, Sunshine JH (2004) RADPEER quality assurance program: a multifacility study of interpretive disagreement rates. Journal of the American College of Radiology 1: 59-65.
- Kim YW, Mansfield LT (2014) Fool Me Twice: Delayed Diagnoses in Radiology with Emphasis on Perpetuated Errors. American Journal of Roentgenology 202: 465-470.
- Robinson PJ (1997) Radiology's Achilles' heel: error and variation in the interpretation of the Röntgen image. The British Journal of Radiology 70: 1085-1098.
- Renfrew DL, Franken EA, Berbaum KS, Weigelt FH, Abu-Yousef MM (1992) Error in radiology: classification and lessons in 182 cases presented at a problem case conference. Radiology 183: 145-150.
- Pinto A (2010) Spectrum of diagnostic errors in radiology. World Journal of Radiology 2: 377.
- Wakeley CJ, Jones AM, Kabala JE, Prince D, Goddard PR (1995) Audit of the value of double reading magnetic resonance imaging films. The British Journal of Radiology 68: 358-360.
- Quekel LG, Kessels AG, Goei R, van Engelshoven JM (1999) Miss rate of lung cancer on the chest radiograph in clinical practice. Chest 115: 720-724.
- Scott WJ, Howington J, Feigenberg S, Movsas B, Pisters K (2007) Treatment of non-small cell lung cancer stage I and stage II: ACCP evidence-based clinical practice guidelines. Chest 132: 2340S-2342S.
- Lodwick GS, Haun CL, Smith WE, Keller RF, Robertson ED (1963) Computer diagnosis of primary bone tumors: A preliminary report. Radiology 80: 273-275.
- Winsberg F, Elkin M, Macy Jr J, Bordaz V, Weymouth W (1967) Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology 89: 211-215.
- Kruger RP, Townes JR, Hall DL, Dwyer SJ, Lodwick GS (1972) Automated radiographic diagnosis via feature extraction and classification of cardiac size and shape descriptors. IEEE Transactions on Biomedical Engineering, pp: 174-186.
- Meyers PH, Nice CM, Becker HC, Nettleton WJ, Sweeney JW, et al. (1964) Automated computer analysis of radiographic images. Radiology 83: 1029-1034.
- Ramesh AN, Kambhampati C, Monson JR, Drew PJ (2004) Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England 86: 334.
- Ding S, Li H, Su C, Yu J, Jin F (2013) Evolutionary artificial neural networks: a review. Artificial Intelligence Review 39: 251-260.
- U.S.FDA (2012) Administration, Guidance Documents (Medical Devices and Radiation Emitting Products)-Guidance for Industry and FDA Staff-Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data-Premarket Approval (PMA) and Premarket Notification [510(k)] Submissions.
- Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized Medical Imaging and Graphics 31: 198-211.
- Castellino RA (2005) Computer aided detection (CAD): an overview. Cancer Imaging 5: 17.
- Morton MJ, Whaley DH, Brandt KR, Amrami KK (2006) Screening mammograms: interpretation with computer-aided detection-prospective evaluation. Radiology 239: 375-383.
- Dean JC, Ilvento CC (2006) Improved cancer detection using computer-aided detection with diagnostic and screening mammography: prospective study of 104 cancers. American Journal of Roentgenology 187: 20-28.
- Ko JM, Nicholas MJ, Mendel JB, Slanetz PJ (2006) Prospective assessment of computer-aided detection in interpretation of screening mammography. American Journal of Roentgenology 187: 1483-1491.
- Yu S, Guan L (2000) A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Transactions on Medical Imaging 19: 115-126.
- Nishikawa RM (2007) Current status and future directions of computer-aided diagnosis in mammography. Computerized Medical Imaging and Graphics 31: 224-235.
- Georgian-Smith D, Moore RH, Halpern E, Yeh ED, Rafferty EA, et al. (2007) Blinded comparison of computer-aided detection with human second reading in screening mammography. American Journal of Roentgenology 189: 1135-1141.
- Taylor P, Potts HW (2008) Computer aids and human second reading as interventions in screening mammography: two systematic reviews to compare effects on cancer detection and recall rate. European Journal of Cancer 44: 798-807.
- Gilbert FJ, Astley SM, McGee MA, Gillan MG, Boggis CR, et al. (2006) Single reading with computer-aided detection and double reading of screening mammograms in the United Kingdom National Breast Screening Program. Radiology 241: 47-53.
- Gilbert FJ, Astley SM, Gillan MG, Agbaje OF, Wallis MG, et al. (2008) CADET II group. CADET II: A prospective trial of computer-aided detection (CAD) in the UK Breast Screening Programme. Journal of Clinical Oncology 26: 508.
- Goddard P, Leslie A, Jones A, Wakeley C, Kabala J (2001) Error in radiology. The British Journal of Radiology 74: 949-951.
- Heuvers ME, Hegmans JP, Stricker BH, Aerts JG (2012) Improving lung cancer survival; time to move on. BMC Pulmonary Medicine 12: 77.
- Li F, Sone S, Abe H, MacMahon H, Armato SG, et al. (2002) Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. Radiology 225: 673-683.
- Kligerman S, Cai L, White CS (2013) The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph. Journal of Thoracic Imaging 28: 244-252.
- Das M, Mühlenbruch G, Mahnken AH, Flohr TG, Gündel L, et al. (2006) Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance. Radiology 241: 564-571.
- Yuan R, Vos PM, Cooperberg PL (2006) Computer-aided detection in screening CT for pulmonary nodules. American Journal of Roentgenology 186: 1280-1287.
- Liang M, Tang W, Xu DM, Jirapatnakul AC, Reeves AP, et al. (2016) Low-dose CT screening for lung cancer: computer-aided detection of missed lung cancers. Radiology 281: 279-288.
- Sahiner B, Chan HP, Hadjiiski LM, Cascade PN, Kazerooni EA, et al. (2009) Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size. Academic Radiology 16: 1518-1530.
- Shiraishi J, Li Q, Appelbaum D, Doi K (2011) Computer-aided diagnosis and artificial intelligence in clinical imaging. In Seminars in Nuclear Medicine 41: 449-462.
- Shiraishi J, Appelbaum D, Pu Y, Li Q, Pesce L, et al. (2007) Usefulness of temporal subtraction images for identification of interval changes in successive whole-body bone scans: JAFROC analysis of radiologists’ performance. Academic Radiology 14: 959-966.
- Yang SK, Moon WK, Cho N, Park JS, Cha JH, et al. (2007) Screening mammography–detected cancers: sensitivity of a computer-aided detection system applied to full-field digital mammograms. Radiology 244: 104-111.
- Friedemann B, Uwe F, Karim B, Serge M, Silivia O, et al. (2003) Computer aided detection (CAD) in direct digital full field mammography. In Digital Mammography, pp: 253-256.
- Lee N, Laine AF, Márquez G, Levsky JM, Gohagan JK (2009) Potential of computer-aided diagnosis to improve CT lung cancer screening. IEEE Reviews in Biomedical Engineering 2: 136-146.
- Armato SG, Giger ML, Moran CJ, Blackburn JT, Doi K, et al. (1999) Computerized detection of pulmonary nodules on CT scans. Radiographics 19: 1303-1311.
- Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T (2001) Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Transactions on Medical Imaging 20: 595-604.
- Suzuki K, Armato SG, Li F, Sone S (2003) Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low‐dose computed tomography. Medical Physics 30: 1602-1617.
- Farag AA, El-Baz A, Gimel’farb G, El-Ghar MA, Eldiasty T (2005) Quantitative nodule detection in low dose chest CT scans: new template modeling and evaluation for CAD system design. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp: 720-728.
- Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Medical Image Analysis 14: 390-406.
- De Hoop B, De Boo DW, Gietema HA, Van Hoorn F, Mearadji B, et al. (2010) Computer-aided detection of lung cancer on chest radiographs: effect on observer performance. Radiology 257: 532-540.
- Setio AA, Ciompi F, Litjens G, Gerke P, Jacobs C, et al. (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging 35: 1160-1169.
- Teach RL, Shortliffe EH (1981) An analysis of physician attitudes regarding computer-based clinical consultation systems. In Use and Impact of Computers in Clinical Medicine, pp: 68-85.
- Pinto A, Brunese L, Pinto F, Reali R, Daniele S, et al. (2012) The concept of error and malpractice in radiology. In Seminars in Ultrasound, CT and MRI 33: 275-279.
- Guerriero C, Gillan MG, Cairns J, Wallis MG, Gilbert FJ (2011) Is computer aided detection (CAD) cost effective in screening mammography? A model based on the CADET II study. BMC Health Services Research 11: 11.
- (2016) IBM makes a quantum processor available for use online. Physics Today.
- Doi K (2005) Current status and future potential of computer-aided diagnosis in medical imaging. The British Journal of Radiology 78: s3-s19.
- Wolf M, Krause J, Carney PA, Bogart A, Kurvers RH (2015) Collective intelligence meets medical decision-making: the collective outperforms the best radiologist. PLoS One 10: e0134269.
- Palmer DW, Piraino DW, Obuchowski NA, Bullen JA (2014) Emergent diagnoses from a collective of radiologists: algorithmic versus social consensus strategies. In International Conference on Swarm Intelligence, pp: 222-229.
- Dubey RB, Hanmandlu M (2012) Integration of CAD into PACS, 2012 2nd International Conference on Power, Control and Embedded Systems, Institute of Electrical and Electronics Engineers (IEEE).
- Le AH, Liu B, Huang HK (2009) Integration of computer-aided diagnosis/detection (CAD) results in a PACS environment using CAD–PACS toolkit and DICOM SR. International Journal of Computer Assisted Radiology and Surgery 4: 317-329.
- Dayhoff JE, DeLeo JM (2001) Artificial neural networks: opening the black box. Cancer: Interdisciplinary International Journal of the American Cancer Society 91: 1615-1635.
Citation: Chérif L, Ayadi H, Khmakhem K, Kacem IH, Kammoun S, et al. (2018) Problematic Video Game Use among Teenagers in Sfax, Tunisia. J Health Educ Res Dev 6: 268. DOI: 10.4172/2380-5439.1000268
Copyright: © 2018 Chérif L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Select your language of interest to view the total content in your interested language
Share This Article
- Total views: 478
- [From(publication date): 0-0 - Feb 21, 2019]
- Breakdown by view type
- HTML page views: 438
- PDF downloads: 40